{"schema_version":"onlylabs.public_analysis_evidence.v1","title":"Snowflake (Arctic) analysis evidence pack","description":"Public onlylabs evidence pack for cited agent analysis: captured pages, ranked public signals, and stored web-search provenance used by the background analysis workflow.","url":"https://onlylabs.fyi/labs/snowflake","json_url":"https://onlylabs.fyi/analysis/snowflake/evidence.json","generated_at":"2026-06-11T13:52:35.176Z","org":{"slug":"snowflake","name":"Snowflake (Arctic)","category":"neocloud","category_label":"Neocloud","dossier_url":"https://onlylabs.fyi/labs/snowflake"},"analysis":null,"workflow":{"version":"onlylabs-deepagents-analysis-v3","provider":null,"model":null,"agent":null,"public_pack_mode":"local-pages-and-events","live_web_fetches":false,"note":"Public evidence exports do not trigger live Exa calls; stored Exa provenance is included when analysis metadata contains it."},"stats":{"pages":28,"events":140,"web":0,"evidence":88,"signal_desks":{"hiring":47,"forks":0,"releases":0,"talking":12,"repos":1},"data_radar_lanes":null,"data_radar_matches":null,"stored_analysis_evidence":null,"stored_analysis_web":null,"stored_analysis_signal_desks":null,"stored_analysis_data_radar_lanes":null,"stored_analysis_data_radar_matches":null},"stored_web_provenance":null,"evidence":[{"ref":"P1","kind":"page","title":"Principal Solution Engineer","date":"2026-06-11T07:03:57.021273+00:00","date_source":null,"source_url":"https://jobs.ashbyhq.com/snowflake/f6cae3b2-2e82-4cdf-bce6-75cdbfb03815","signal_url":null,"signal_json_url":null,"text":"# Principal Solution Engineer\n\nTeam: Solution Engineering\n\nLocation: US-GA-Remote\n\nEmployment type: FullTime\n\nWorkplace type: Remote\n\nRemote: yes\n\nPublished: 2026-05-22T15:37:01.326+00:00\n\nAt Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done.\n\nWe are looking for a Principal Solution Engineer who is accustomed to solving customer’s most complex problems and closing large deals. In this role you will work directly with the sales team and channel partners to understand the needs of our customers, strategize on how to navigate winning sales cycles, provide compelling value-based demonstrations, support enterprise Proof of Concepts, and ultimately close business.\n\nAs a Snowflake Solution Engineer you must share our passion about reinventing the database space, thrive in a dynamic environment and have the flexibility and willingness to jump in and get things done. You are equally comfortable in both a business and technical context, interacting with executives and talking shop with technical audiences.\n\nIN THIS ROLE YOU WILL GET TO:\n\n- Present Snowflake technology and vision to executives and technical contributors at prospects and customers\n\n- Work hands-on with prospects and customers to demonstrate and communicate the value of Snowflake technology throughout the sales cycle, from demo to proof of concept to design and implementation\n\n- Immerse yourself in the ever-evolving industry, maintaining a deep understanding of competitive and complementary technologies and vendors and how to position Snowflake in relation to them.\n\n- "},{"ref":"P2","kind":"page","title":"Senior Corporate Counsel 1 - Intellectual Property","date":"2026-06-11T07:03:56.913854+00:00","date_source":null,"source_url":"https://jobs.ashbyhq.com/snowflake/bff9c418-0ec4-45c6-8d5c-9917fa1631ad","signal_url":null,"signal_json_url":null,"text":"# Senior Corporate Counsel 1 - Intellectual Property\n\nTeam: Legal\n\nLocation: US-CA-Menlo Park\n\nEmployment type: FullTime\n\nWorkplace type: Hybrid\n\nRemote: yes\n\nPublished: 2026-06-04T22:34:30.532+00:00\n\nAt Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done.\n\nWe are looking for a driven and collaborative IP attorney to join Snowflake's growing Intellectual Property team. Reporting to the Director, Assistant General Counsel – Intellectual Property, this role will be a core member of a lean, high-impact IP team where you will have direct ownership over meaningful work from day one. You will manage Snowflake's patent portfolio, lead patent prosecution strategy, and support pre-litigation and IP litigation matters. This is an exceptional opportunity for a patent attorney who brings both prosecution and litigation experience, thrives in a fast-moving technology environment, and is eager to grow their practice into adjacent areas such as open source.\n\nThis is a hybrid role based in Menlo Park, CA or Dublin, CA with a preference for Menlo Park where the majority of the IP team is based.\n\nRESPONSIBILITIES\n\n- Manage Snowflake's global patent portfolio, including overseeing prosecution strategy, outside counsel relationships, and budget\n\n- Lead and continuously improve Snowflake's internal patent program, including inventor education, invention submission, and evaluation processes\n\n- Review and supervise outside patent counsel on the drafting of new patent applications and prosecution strategy across Snowflake's patent portfolio, workin"},{"ref":"P3","kind":"page","title":"Senior Solution Engineer","date":"2026-06-11T07:03:56.786051+00:00","date_source":null,"source_url":"https://jobs.ashbyhq.com/snowflake/850505b0-50f8-405a-868e-b9957453047b","signal_url":null,"signal_json_url":null,"text":"# Senior Solution Engineer\n\nTeam: Solution Engineering\n\nLocation: US-GA-Atlanta\n\nEmployment type: FullTime\n\nPublished: 2026-06-08T12:35:55.087+00:00\n\nAt Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done.\n\nWe are looking for a Senior Solution Engineer who is accustomed to solving customer’s most complex problems and closing large deals. In this role you will work directly with the sales team and channel partners to understand the needs of our customers, strategize on how to navigate winning sales cycles, provide compelling value-based demonstrations, support enterprise Proof of Concepts, and ultimately close business.\n\nAs a Snowflake Solution Engineer you must share our passion about reinventing the database space, thrive in a dynamic environment and have the flexibility and willingness to jump in and get things done. You are equally comfortable in both a business and technical context, interacting with executives and talking shop with technical audiences.\n\nIN THIS ROLE YOU WILL GET TO:\n\n- Present Snowflake technology and vision to executives and technical contributors at prospects and customers\n\n- Work hands-on with prospects and customers to demonstrate and communicate the value of Snowflake technology throughout the sales cycle, from demo to proof of concept to design and implementation\n\n- Immerse yourself in the ever-evolving industry, maintaining a deep understanding of competitive and complementary technologies and vendors and how to position Snowflake in relation to them.\n\n- Collaborate with Product Management, Engin"},{"ref":"P4","kind":"page","title":"Cloud Infrastructure Engineer","date":"2026-06-11T07:03:56.722694+00:00","date_source":null,"source_url":"https://jobs.ashbyhq.com/snowflake/4fe8d816-0453-4d63-ae0a-2085a5a49101","signal_url":null,"signal_json_url":null,"text":"# Cloud Infrastructure Engineer\n\nTeam: Enterprise Technology\n\nLocation: US-CA-Dublin\n\nEmployment type: FullTime\n\nWorkplace type: Hybrid\n\nRemote: yes\n\nPublished: 2026-06-05T19:09:06.632+00:00\n\nAt Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done.\n\nWe are hiring a cloud infrastructure engineer for our Enterprise Cloud Engineering team. Our Enterprise Cloud Engineering team builds and operates the foundational identity, access, cloud infrastructure, and security platforms that enable employees, applications, services, and AI systems to securely operate at scale. We own the reliability, scalability, and security of critical identity services, cloud platforms, Kubernetes environments, and production workloads across AWS and Azure, with a strong focus on automation, operational excellence, and secure-by-default design. Partnering closely with Security, Infrastructure, and Engineering teams, we design and deliver authentication, authorization, identity governance, cloud access controls, and AI security capabilities while also deploying and supporting business-critical applications and services. As stewards of platform reliability, we leverage modern engineering practices, infrastructure as code, observability, and site reliability principles to ensure our systems remain resilient, performant, compliant, and ready to support the company's growth.\n\nAS A CLOUD INFRASTRUCTURE ENGINEER, YOU WILL:\n\n- Design, build, and operate secure, scalable cloud infrastructure and identity platforms across AWS and Azure.\n\n- Implement and manage IAM"},{"ref":"P5","kind":"page","title":"Principal Product Manager - Zero-Copy Integrations","date":"2026-06-11T07:03:56.58497+00:00","date_source":null,"source_url":"https://jobs.ashbyhq.com/snowflake/0131b6dc-50b8-48bc-856a-b797554b484d","signal_url":null,"signal_json_url":null,"text":"# Principal Product Manager - Zero-Copy Integrations\n\nTeam: Product Management\n\nLocation: US-WA-Bellevue\n\nEmployment type: FullTime\n\nPublished: 2026-06-09T17:50:12.657+00:00\n\nAt Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done.\n\nSnowflake is redefining how enterprises bring data, applications, and AI together. As customers build AI systems for insights, automation, and action, they need governed access to the most important business data in the enterprise: data from systems like SAP, Salesforce, Workday, ServiceNow, other leading application platforms.\n\nHistorically, integrating this data meant building pipelines, duplicating data, losing business semantics, and creating operational complexity. The next generation of enterprise AI requires a better pattern: open, governed, zero-copy interoperability across application platforms, open table formats, catalogs, semantics, and Snowflake.\n\nWe are looking for a hands-on, high-conviction Principal Product Manager to lead and scale Snowflake’s Zero-Copy Integrations product stream. This person will help shape the product strategy, execution model, partner roadmap, commercial motion, and customer experience for how Snowflake interoperates with the world’s most important business application platforms.\n\nThis is a highly strategic role at the intersection of product, engineering, design, partnerships, GTM, and executive-level customer engagement. You will work closely with leading application vendors and Snowflake teams to define how customers access structured and unstructured busi"},{"ref":"P6","kind":"page","title":"SnowCAT Technical Principal","date":"2026-06-11T07:03:56.581903+00:00","date_source":null,"source_url":"https://jobs.ashbyhq.com/snowflake/9c44b1c2-f3f9-4e64-9540-bbc06dcbefda","signal_url":null,"signal_json_url":null,"text":"# SnowCAT Technical Principal\n\nTeam: Product Management\n\nLocation: US-USA-Remote\n\nEmployment type: FullTime\n\nWorkplace type: Hybrid\n\nRemote: yes\n\nPublished: 2026-06-09T02:07:51.241+00:00\n\nAt Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done.\n\nAbout this Position\n\nWe are looking for a senior leader in the cloud data platform space to join our team and help shape some of the most cutting edge features at Snowflake! This individual will work collaboratively and hands-on with Product Management and Engineering to accelerate customers adopting our most challenging features and architectures. We are looking for an experienced professional capable of both applying their past experiences to new architectures and using their aptitude to learn the next new wave of technologies that can benefit customers on the Snowflake Data Cloud.\n\nThis position will be a part of the SnowCAT team (Customer Acceleration Team) within the product organization. SnowCAT is a group that brings deep experience with database, application, container, machine learning, and AI technologies to investigate the newest, most demanding customer scenarios and validate emerging features and capabilities in the Snowflake product. SnowCAT team members work closely with product managers, engineering leaders, and a variety of field organizations and customers to turn today’s ‘bleeding edge’ into tomorrow’s mainstream.\n\nAS TECHNICAL PRINCIPAL ON THE SNOWCAT TEAM YOU WILL:\n\n- Impact Snowflake’s product success in multiple market segments and workloads.\n\n- Engage with PM a"},{"ref":"P7","kind":"page","title":"Associate Solution Engineer","date":"2026-06-11T07:03:56.458789+00:00","date_source":null,"source_url":"https://jobs.ashbyhq.com/snowflake/c0d03ec9-2bfa-497b-b6a6-e045d1801206","signal_url":null,"signal_json_url":null,"text":"# Associate Solution Engineer\n\nTeam: Solution Engineering\n\nLocation: SE-Stockholm-MSO\n\nEmployment type: FullTime\n\nPublished: 2026-06-10T15:26:10.809+00:00\n\nAt Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done.\n\nSnowflake is built on a culture of impact and innovation. We are looking for an Associate Solution Engineer who is ready to solve complex data challenges and help enterprises realize the full potential of the AI Data Cloud.\n\nIn this role, you will partner with account teams to understand customer needs, strategize on technical evaluations, and demonstrate why Snowflake is the premier platform for the AI and data frontier.\n\nTHE ROLE\n\n- Present & Vision-Set: Deliver Snowflake’s technology story to everyone from technical contributors to C-suite executives.\n\n- Hands-on Proof: Conduct deep-dive demos and Proofs of Concept (POCs) that translate features into tangible business value.\n\n- Healthy Consumption: Act as a trusted advisor to ensure customers are using Snowflake efficiently and effectively, driving long-term value and sustainable growth.\n\n- Strategic Partnership: Collaborate with Solution Engineers, Product, and Engineering teams to drive technical wins and refine our product roadmap.\n\n- Market Mastery: Stay at the cutting edge of the industry, positioning Snowflake effectively against competitive and complementary technologies.\n\nWHAT WE ARE LOOKING FOR\n\nAlthough it is a huge plus, we don’t require prior Solution Engineering experience for this associate role. Instead, we are looking for the right traits, hunger,"},{"ref":"P8","kind":"page","title":"Solution Engineer","date":"2026-06-11T07:03:56.286515+00:00","date_source":null,"source_url":"https://jobs.ashbyhq.com/snowflake/1c9830cf-4309-49e5-abdd-0906f60ddbd2","signal_url":null,"signal_json_url":null,"text":"# Solution Engineer\n\nTeam: Solution Engineering\n\nLocation: SE-Stockholm-MSO\n\nEmployment type: FullTime\n\nPublished: 2026-06-10T15:26:37.100+00:00\n\nAt Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done.\n\nSnowflake is built on a culture of impact and innovation. We are looking for a Solution Engineer who is ready to solve complex data challenges and help enterprises realize the full potential of the AI Data Cloud.\n\nIn this role, you will partner with account teams to understand customer needs, strategize on technical evaluations, and demonstrate why Snowflake is the premier platform for the AI and data frontier.\n\nTHE ROLE\n\n- Present & Vision-Set: Deliver Snowflake’s technology story to everyone from technical contributors to C-suite executives.\n\n- Hands-on Proof: Conduct deep-dive demos and Proofs of Concept (POCs) that translate features into tangible business value.\n\n- Healthy Consumption: Act as a trusted advisor to ensure customers are using Snowflake efficiently and effectively, driving long-term value and sustainable growth.\n\n- Strategic Partnership: Collaborate with Solution Engineers, Product, and Engineering teams to drive technical wins and refine our product roadmap.\n\n- Market Mastery: Stay at the cutting edge of the industry, positioning Snowflake effectively against competitive and complementary technologies.\n\nWHAT WE ARE LOOKING FOR\n\nAlthough it is a huge plus, we don’t require prior Solution Engineering experience for this associate role. Instead, we are looking for the right traits, hunger, and a specific \"X Fa"},{"ref":"P9","kind":"page","title":"Sr. Manager, Resource Delivery Manager","date":"2026-06-11T07:03:56.283137+00:00","date_source":null,"source_url":"https://jobs.ashbyhq.com/snowflake/a4dea157-34aa-4830-8894-4696f1dd843f","signal_url":null,"signal_json_url":null,"text":"# Sr. Manager, Resource Delivery Manager\n\nTeam: Professional Services\n\nLocation: US-IL-Chicago-MSO\n\nEmployment type: FullTime\n\nWorkplace type: Remote\n\nRemote: yes\n\nPublished: 2026-06-10T18:43:55.171+00:00\n\nAt Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done.\n\nJOB SUMMARY\n\nAs the Sr. Manager of Resource Delivery, you will lead the Resource Delivery Management team for Service Delivery and set the strategy for scalable, AI-enabled fulfillment. You will define the roadmap for internal and external resource fulfillment, talent allocation, and automation, ensuring direct alignment to Snowflake’s annual revenue and delivery goals. In this role, you will elevate Resource Delivery from a transactional fulfillment function to a strategic advisory capability that uses automation, AI, and operational rigor to bring insight to action faster.\n\nYou will lead a team of Global Fulfillment Managers and leverage Snowflake technology and internal AI tools, including CoCo (Cortex Code), Raven, and emerging capabilities, to improve fulfillment speed, resource quality, and decision-making. We are looking for a leader who is comfortable with SQL and coding and who actively embraces Snowflake’s AI ecosystem to transform how fulfillment work gets done.\n\nKEY RESPONSIBILITIES\n\n1. Strategic Leadership & Governance\n\n- Define Strategy & Roadmap: Define the long-term roadmap for resource delivery, talent allocation, partner fulfillment, and automation, aligned to annual revenue and delivery goals.\n\n- Build Scalable Operating Models: Establish global po"},{"ref":"P10","kind":"page","title":"Hybrid Tables Just Got Up to 8x Faster","date":"2026-06-11T07:03:55.882216+00:00","date_source":null,"source_url":"https://www.snowflake.com/content/snowflake-site/global/en/blog/hybrid-tables-performance-improvements","signal_url":null,"signal_json_url":null,"text":"Hybrid Tables Just Got Up to 8x Faster \n\nSkip to content \n\nBlog / Product and Technology / Hybrid Tables Just Got Up To 8x Faster \n\nJUN 10, 2026 / 4 min read Product and Technology \nHybrid Tables Just Got Up To 8x Faster \n\nClaire Peracchio +2 \n\nHybrid Tables just got up to 8x faster (based on internal benchmarks), 1 with standardized billing and dramatically improved batch performance. This breakthrough makes Hybrid Tables even more performant for high-concurrency, low-latency workloads like AI apps and workflow state management.\n\nHere’s what this means in practice: Teams can now run thousands of concurrent point lookups, store AI agent state and manage transactional application logic directly on Snowflake, at speeds that previously required a separate, dedicated database. \n\nTransactional workloads shouldn't require complex data movement \n\nFor too long, teams building transactional applications have been forced to maintain and connect separate online transaction processing (OLTP) databases alongside their analytical platform. This creates painful complexity: brittle pipelines, data inconsistency, and duplicated governance and engineering hours spent syncing systems instead of building products.\n\nIn the age of AI agents and real-time applications, this fragmentation can be a liability. When your agent needs to read fresh transactional state, query historical context and write back results — all in milliseconds — you can't afford the latency of data pipelines.\n\nHybrid Tables address this by deeply integrating transactional workloads into the Snowflake database, allowing you to join transactional and analytical data in a single query, while maintaining unified governance and security controls across all your data. \n\nWhat's new: Performance that changes what's possible\n\nThis release encompasses three major improvements.\n\n1. Higher throughput, applied automatically\n\nHybrid Tables now support single-statement query execution that can dramatically reduce overhead for repetitive operations. The results: up to 8x higher throughput for point operations, based on internal benchmarks. 1 \n\nOnce generally available, this performance improvement will be enabled by default for "},{"ref":"P11","kind":"page","title":"AI Data Engineering: New Smart Pipelines in Snowflake","date":"2026-06-11T07:03:55.844384+00:00","date_source":null,"source_url":"https://www.snowflake.com/content/snowflake-site/global/en/blog/ai-smart-pipelines-whats-new","signal_url":null,"signal_json_url":null,"text":"AI Data Engineering: New Smart Pipelines in Snowflake \n\nSkip to content \n\nBlog / Product and Technology / Data Engineering in the AI Era: New Snowflake Tools Built for Smart Pipelines \n\nJUN 10, 2026 / 10 min read Product and Technology \nData Engineering in the AI Era: New Snowflake Tools Built for Smart Pipelines \n\nAbhishek Kashyap +1 \n\nAI has made it easier than ever to build. However, easier to build is not the same as built to last. If you have brittle, fragile systems, AI is only going to make it worse, not better. That's why you need a platform built to make the most of AI.\n\nAt Snowflake Summit 2026, we announced new capabilities that put our customers at the forefront of data engineering today. We've added AI directly into workflows and made it easier to build data pipelines from start to finish. These new features are designed for every type of data engineer. They work where your data lives: in Snowflake, in open and interoperable lakehouses or both. Whether you write SQL, Python or build ML models, everything you need to construct pipelines exists in one place. With Snowflake, you get elastic compute performance that scales, seamless connectivity to data wherever it lives, and enterprise-grade governance capabilities for secure, trusted data with consistent business context.\n\nFaster time-to-production with AI\n\nFigure 1: Snowflake CoCo outperforms generic coding agents for data engineering tasks. 1 \n\nWith new agentic workflows, AI operates directly within your local environment to build end-to-end solutions. For real data engineering work, Snowflake CoCo sets the bar for leading coding agents. Benchmarks comparing to Claude Code running on Opus 4.7, for instance, show that CoCo uses 51% fewer tokens and takes 8% fewer steps to get the job done. 2 \n\nBringing context-aware assistance and purpose-built skills for Snowflake data engineering features, CoCo operates within your security perimeter and crucially understands your enterprise data context. With access to the latest models, like Claude Opus 4.8, Claude Sonnet 4.6 and GPT 5.5, data engineers can use it in Snowsight, through the CoCo CLI or now through a new desktop app (public preview). Use prebuilt o"},{"ref":"P12","kind":"page","title":"Account Executive, Public Sector-SLED","date":"2026-06-11T07:03:55.742843+00:00","date_source":null,"source_url":"https://jobs.ashbyhq.com/snowflake/2bb5f38e-b7b0-4a45-93d1-29538e9b1b73","signal_url":null,"signal_json_url":null,"text":"# Account Executive, Public Sector-SLED\n\nTeam: Sales\n\nLocation: US-GA-Atlanta\n\nEmployment type: FullTime\n\nPublished: 2026-06-10T20:37:37.669+00:00\n\nAt Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done.\n\nAS AN ACCOUNT EXECUTIVE, PUBLIC SECTOR, SLED AT SNOWFLAKE, YOU WILL:\n\n- Develop strategic relationships, identify and close new business opportunities within Public Sector organizations on the state, local, and university levels.\n\n- Be the trusted advisor to the customer at the most senior levels and drive thought leadership by understanding their existing and future IT roadmap to drive the Snowflake solution within their organization.\n\n- Challenge the customers status-quo around their current operations.\n\n- Propose solutions which enable and align to the customers‘ digital transformation roadmaps.\n\n- Collaborate with marketing and the partner organization to develop high-level plans to drive revenue growth.\n\n- Master a solution-based approach to selling with experience managing complex sales processes.\n\nWE ARE LOOKING FOR SOMEONE WITH:\n\n- 10+ years of full cycle sales experience. An emphasis on cloud, databases, business intelligence software, data warehousing, and SaaS is preferred.\n\n- Proven ability to independently develop, manage and close new client relationships at CxO level in Public Sector organizations.\n\n- Extensive and reliable network with key decision makers in the Public Sector as well as with key partners in this field and experience in building relationships with these Economic Buyers and Executive Sponsors."},{"ref":"P13","kind":"page","title":"Snowflake-Labs/sfguide-customer-issue-deduplication-demo repository metadata","date":"2026-06-11T04:07:58.084894+00:00","date_source":null,"source_url":"https://github.com/Snowflake-Labs/sfguide-customer-issue-deduplication-demo","signal_url":null,"signal_json_url":null,"text":"# Snowflake-Labs/sfguide-customer-issue-deduplication-demo\n\nDescription: Using Snowflake AISQL for entity identification and issue deduplication\n\nLanguage: Jupyter Notebook\n\nLicense: Apache-2.0\n\nStars: 2\n\nForks: 0\n\nOpen issues: 0\n\nCreated: 2025-10-17T02:14:14Z\n\nPushed: 2026-03-07T23:11:41Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# Smart Complaint deduplication using Snowflake-native AISQL\n\nBy Anant Damle & Nathan Birch\n\nStory Link: [https://medium.com/snowflake/smart-complaint-deduplication-using-snowflake-native-aisql-2bab5885e277](https://medium.com/snowflake/smart-complaint-deduplication-using-snowflake-native-aisql-2bab5885e277)\n\nCustomer issue management is an inevitable part of doing business in today’s world. Duplicate complaints remain a common\nchallenge for organisations, often stemming from customers following up on similar issues through different channels and\nlack of reliable master account information, leading to frustrating experiences for both customers and support teams,\nand ultimately, skewed reporting.\n\nImagine a customer raising a complaint via a webform, then following up with a phone call about the same issue.\nWithout a robust system, the on-call person might be unaware of the previous interaction, leading to redundant efforts\nand a lack of visibility into the true state of a customer's issue. The Challenge: unreliable matching, lost visibility\nand wasted resources.\n\nTraditional methods often rely on name/entity resolution, extracting attributes like name, phone number, or email using\nregex or other forms of patterns and then match to related issues. However, these identifiers aren't always reliable.\nAccount numbers or License Plate Numbers (LPNs) can be inconsistent, and even within a short timeframe (e.g., 5 days),\nthe same issue might be treated as a new complaint. The result? Duplicate reports that obscure the real count of unique\nissues, hindering effective analysis and decision-making.\n\n# Architecture\n\nA Snowflake-native solution designed to address these deduplication challenges, provides a clear and accurate picture of\ncustomer complaints.\n\n```mermaid\n---\nconfig:\ntheme: 'default'\nthemeVariables:\ndarkMode: false\nfontSiz"},{"ref":"P14","kind":"page","title":"Snowflake-Labs/sfguide-getting-started-openflow-kafka-connector repository metadata","date":"2026-06-11T04:07:57.815106+00:00","date_source":null,"source_url":"https://github.com/Snowflake-Labs/sfguide-getting-started-openflow-kafka-connector","signal_url":null,"signal_json_url":null,"text":"# Snowflake-Labs/sfguide-getting-started-openflow-kafka-connector\n\nDescription: Companion repo for Quickstart Getting Started with Snowflake Openflow Kafka Connector\n\nLanguage: Jupyter Notebook\n\nLicense: NOASSERTION\n\nStars: 1\n\nForks: 1\n\nOpen issues: 1\n\nCreated: 2025-10-16T04:46:28Z\n\nPushed: 2025-10-17T14:05:59Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# Getting Started with Snowflake Openflow Kafka Connector\n\nCompanion repository for the [Snowflake Openflow Kafka Connector Quickstart](https://quickstarts.snowflake.com/guide/getting_started_with_openflow_kafka_connector/index.html).\n\n## Overview\n\nBuild a real-time streaming pipeline from Apache Kafka to Snowflake using Openflow. Stream application logs from Kafka to Snowflake with automatic schema detection, schema evolution, and perform powerful SQL analytics including semantic search with Cortex Search.\n\n**📖 [Follow the Complete Quickstart Guide](https://quickstarts.snowflake.com/guide/getting_started_with_openflow_kafka_connector/index.html)**\n\n## Repository Contents\n\n```\n.\n├── README.md # This file\n├── RPK_CLI_README.md # Detailed RPK CLI setup guide\n├── Taskfile.yml # Task runner for common operations\n├── LICENSE.txt # Apache 2.0 license\n├── pyproject.toml # Python project dependencies\n├── .env.template # Environment variable template\n├── sql/\n│ ├── 1.snowflake_setup.sql # Snowflake environment setup\n│ ├── 2.verify_ingestion.sql # Data ingestion verification\n│ ├── 2a.verify_base_schema.sql # Verify base schema ingestion\n│ ├── 2b.verify_schema_evolution.sql # Verify schema evolution\n│ ├── 3.analytics_queries.sql # Example analytics queries\n│ ├── 4.cortex_search.sql # Semantic search with Cortex Search\n│ └── 5.cleanup.sql # Cleanup script\n└── sample-data/\n├── sample_logs.json # 50 base schema sample records\n├── sample_logs_evolved.json # 80 evolved schema sample records\n└── generate_logs.py # Python log generator script\n```\n\n## Quick Start\n\n### 1. Clone and Setup\n\n```bash\ngit clone https://github.com/Snowflake-Labs/sfguide-getting-started-openflow-kafka-connector.git\ncd sfguide-getting-started-openflow-kafka-connector\nexport QUICK_START_REPO=$PWD\n```\n\n### 2. Install Task Runner (Optional)\n\n```ba"},{"ref":"P15","kind":"page","title":"Snowflake-Labs/sfguide-extracting-insights-from-multimodal-customer-data repository metadata","date":"2026-06-11T04:07:57.666011+00:00","date_source":null,"source_url":"https://github.com/Snowflake-Labs/sfguide-extracting-insights-from-multimodal-customer-data","signal_url":null,"signal_json_url":null,"text":"# Snowflake-Labs/sfguide-extracting-insights-from-multimodal-customer-data\n\nLanguage: Jupyter Notebook\n\nLicense: Apache-2.0\n\nStars: 5\n\nForks: 12\n\nOpen issues: 0\n\nCreated: 2025-10-20T18:50:27Z\n\nPushed: 2025-11-05T22:56:48Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# Snowflake Cortex AI SQL: Extracting Insights from Multimodal Customer Service Data\n\n## Overview\n\nIn this quickstart, you'll learn how to build a comprehensive customer service analytics system that processes audio, text, and document data using Snowflake Cortex AI functions. This application demonstrates how to extract insights from multimodal data sources including call recordings, chat logs, support tickets, and PDF documents using powerful AI capabilities like transcription, translation, sentiment analysis, classification, and intelligent summarization. By the end of this session, you will have a clear understanding of how to use Cortex AI functions to analyze customer service data at scale, all while keeping your data secure within Snowflake's environment.\n\n## Step-By-Step Guide\n\nFor prerequisites, environment setup, step-by-step guide and instructions, please refer to the [QuickStart Guide](https://quickstarts.snowflake.com/guide/extracting-insights-from-multimodal-customer-service-data/index.html?index=..%2F..index#0)."},{"ref":"P16","kind":"page","title":"Snowflake-Labs/sfguide-from-dev-to-production-why-ml-teams-are-migrating-to-snowflake repository metadata","date":"2026-06-11T04:07:57.299524+00:00","date_source":null,"source_url":"https://github.com/Snowflake-Labs/sfguide-from-dev-to-production-why-ml-teams-are-migrating-to-snowflake","signal_url":null,"signal_json_url":null,"text":"# Snowflake-Labs/sfguide-from-dev-to-production-why-ml-teams-are-migrating-to-snowflake\n\nLanguage: Jupyter Notebook\n\nLicense: Apache-2.0\n\nStars: 2\n\nForks: 0\n\nOpen issues: 0\n\nCreated: 2025-10-22T00:24:44Z\n\nPushed: 2026-02-02T23:53:11Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# From Dev to Production: Why ML team are migrating to Snowflake\n\n## Overview\nIn this guide, you'll learn how to build a complete machine learning lifecycle in Snowflake, from model development to production deployment. You'll deploy HuggingFace models, train custom ML models, track experiments, deploy for inference, and enable real-time feature serving. The application addresses end-to-end ML development showing how to do audio processing, feature extraction, model training, deployment, and monitoring all inside Snowflake with unified governance across the application full-stack.\n\n## Step-By-Step Guide\nFor prerequisites, environment setup and instructions, refer to the [QuickStart](https://quickstarts.snowflake.com/guide/from_dev_to_production_why_ml_teams_are_migrating_to_snowflake/index.html?index=..%2F..index#0) Guide."},{"ref":"P17","kind":"page","title":"Snowflake-Labs/build25-snowsight-whatsnew repository metadata","date":"2026-06-11T04:07:57.221656+00:00","date_source":null,"source_url":"https://github.com/Snowflake-Labs/build25-snowsight-whatsnew","signal_url":null,"signal_json_url":null,"text":"# Snowflake-Labs/build25-snowsight-whatsnew\n\nDescription: Companion repository to Snowsight What's New Session at BUILD25!\n\nLicense: Apache-2.0\n\nStars: 7\n\nForks: 1\n\nOpen issues: 0\n\nCreated: 2025-10-31T01:14:12Z\n\nPushed: 2025-11-04T17:17:06Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# BUILD 2025: What's New In Snowsight\n\nSession Info: [Agenda](https://www.snowflake.com/en/build/americas/agenda?agendaPath=session/1752992&utm_campaign=build25-snowsight-whatsnew)\n\nThis is the companion repository to Snowsight What's New Session at BUILD 2025. \n\n### Cost Management and Tag Based Budgets:\n* [Cost Management Documentation](https://docs.snowflake.com/en/user-guide/cost-management-overview&utm_campaign=build25-snowsight-whatsnew)\n* [Getting started with tag based budgets](https://www.snowflake.com/en/engineering-blog/tag-based-budgets-cost-attribution/?utm_campaign=build25-snowsight-whatsnew)\n* [Visit Cost Management in your Snowflake Account](https://app.snowflake.com/_deeplink/#/account/usage?utm_campaign=build25-snowsight-whatsnew)\n\n### Cortex Code\n* [About Cortex Code](https://www.snowflake.com/en/news/press-releases/snowflake-unveils-new-developer-tools-to-supercharge-enterprise-grade-agentic-ai-development/?utm_campaign=build25-snowsight-whatsnew)\n* Prompts:\n\n```\nCreate a new tag group for me called ‘BUSINESS UNIT’\nwith the following values:\n‘Marketing’, ‘Sales’, ‘Engineering’, ‘Product’, ‘Data’\n```\n\n```\nWhat are the top 5 most commonly queried tables\nthat contain PII data?\nLimit your search to the last 7 days\nand give me recommendations for securing the data.\n```\n\n### Workspaces\n* [About Workspaces](https://www.snowflake.com/en/blog/workspaces-ga-sql-development/?utm_campaign=build25-snowsight-whatsnew)\n* [Workspaces Sharing (PrPr)](https://docs.snowflake.com/LIMITEDACCESS/workspaces-shared?utm_campaign=build25-snowsight-whatsnew)\n* [Zero to Snowflake Example](https://github.com/Snowflake-Labs/sfguide-getting-started-from-zero-to-snowflake)\n* [Try Workspaces in your Snowflake Account](https://app.snowflake.com/_deeplink/#/workspaces?utm_campaign=build25-snowsight-whatsnew)\n\n### dbt Projects on Snowflake\n* [Getting started with dbt Projects on Snowflak"},{"ref":"P18","kind":"page","title":"Snowflake-Labs/terraform-snowflake-workload-identity-federation repository metadata","date":"2026-06-11T04:07:57.077981+00:00","date_source":null,"source_url":"https://github.com/Snowflake-Labs/terraform-snowflake-workload-identity-federation","signal_url":null,"signal_json_url":null,"text":"# Snowflake-Labs/terraform-snowflake-workload-identity-federation\n\nDescription: A Terraform module for workload identity federation (WIF) on Snowflake\n\nLanguage: HCL\n\nLicense: Apache-2.0\n\nStars: 0\n\nForks: 0\n\nOpen issues: 1\n\nCreated: 2025-10-22T20:03:13Z\n\nPushed: 2026-05-14T21:19:27Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# Snowflake Workload Identity Federation terraform Module\n\n[![Terraform Validation](https://github.com/Snowflake-Labs/terraform-snowflake-workload-identity-federation/actions/workflows/terraform-validate.yml/badge.svg)](https://github.com/Snowflake-Labs/terraform-snowflake-workload-identity-federation/actions/workflows/terraform-validate.yml)\n[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)\n\nThis module provides a composable method to configure Workload Identity Federation for Snowflake.\n\n## Terraform Technical Documentation\n\n<!-- BEGIN_TF_DOCS -->\n## Requirements\n\n| Name | Version |\n| ---- | ------- |\n| <a name=\"requirement_terraform\"></a> [terraform](#requirement\\_terraform) | >= 1.5.0 |\n| <a name=\"requirement_snowflake\"></a> [snowflake](#requirement\\_snowflake) | >= 2.13.0, < 2.16 |\n\n## Providers\n\n| Name | Version |\n| ---- | ------- |\n| <a name=\"provider_snowflake\"></a> [snowflake](#provider\\_snowflake) | >= 2.13.0, < 2.16 |\n\n## Modules\n\nNo modules.\n\n## Resources\n\n| Name | Type |\n| ---- | ---- |\n| [snowflake_account_role.wif](https://registry.terraform.io/providers/snowflakedb/snowflake/latest/docs/resources/account_role) | resource |\n| [snowflake_grant_account_role.wif_role_to_user](https://registry.terraform.io/providers/snowflakedb/snowflake/latest/docs/resources/grant_account_role) | resource |\n| [snowflake_grant_privileges_to_account_role.wif_role_permissions](https://registry.terraform.io/providers/snowflakedb/snowflake/latest/docs/resources/grant_privileges_to_account_role) | resource |\n| [snowflake_service_user.wif](https://registry.terraform.io/providers/snowflakedb/snowflake/latest/docs/resources/service_user) | resource |\n\n## Inputs\n\n| Name | Description | Type | Default | Required |\n| ---- | ----------- | ---- | ------- | :------: |\n| <a name=\"inpu"},{"ref":"P19","kind":"page","title":"Snowflake-Labs/sfguide-supply-chain-assistant-with-snowflake-intelligence repository metadata","date":"2026-06-11T04:07:57.075873+00:00","date_source":null,"source_url":"https://github.com/Snowflake-Labs/sfguide-supply-chain-assistant-with-snowflake-intelligence","signal_url":null,"signal_json_url":null,"text":"# Snowflake-Labs/sfguide-supply-chain-assistant-with-snowflake-intelligence\n\nLanguage: Python\n\nLicense: MIT\n\nStars: 1\n\nForks: 13\n\nOpen issues: 1\n\nCreated: 2025-10-29T15:29:57Z\n\nPushed: 2025-11-21T18:09:05Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# Supply Chain Assistant with Snowflake Intelligence\n\n## Solution Overview\n\nModern supply chain operations face a critical challenge: efficiently managing raw material inventory across multiple manufacturing facilities. Operations managers must constantly balance inventory levels, deciding whether to transfer materials between plants with excess and shortage, or purchase new materials from suppliers. Making these decisions manually is time-consuming, error-prone, and often results in suboptimal cost outcomes.\n\nThis quickstart demonstrates how to build an intelligent supply chain assistant using Snowflake Intelligence and Cortex AI capabilities. By combining natural language querying with semantic search over both structured and unstructured data, you'll create a complete solution that helps operations managers make data-driven decisions about inventory management.\n\nHere is a summary of what you will be able to learn by following this quickstart:\n\n* **Setup Environment**: Create a comprehensive supply chain database with tables for manufacturing plants, inventory, suppliers, customers, orders, shipments, and weather data\n* **Cortex Analyst**: Build semantic models for supply chain operations and weather forecasts that enable natural language text-to-SQL queries\n* **Cortex Search**: Index unstructured supply chain documentation for intelligent retrieval using RAG (Retrieval Augmented Generation)\n* **Custom Tools**: Integrate web search, web scraping, HTML generation, and email capabilities into your AI agent\n* **Snowflake Intelligence**: Create a comprehensive AI agent with 7 tools that intelligently routes user questions and combines multiple data sources\n* **Advanced Analytics**: Perform complex multi-domain analysis including supply chain optimization, weather impact analysis, and external research\n\n## The Problem\n\n![Alt text](/images/problem.png \"The Problem\")\n\nSupply chain operations managers face daily"},{"ref":"P20","kind":"page","title":"Snowflake-Labs/pg_lake repository metadata","date":"2026-06-11T04:07:56.720156+00:00","date_source":null,"source_url":"https://github.com/Snowflake-Labs/pg_lake","signal_url":null,"signal_json_url":null,"text":"# Snowflake-Labs/pg_lake\n\nDescription: pg_lake: Postgres with Iceberg and data lake access\n\nLanguage: C\n\nLicense: Apache-2.0\n\nStars: 1544\n\nForks: 102\n\nOpen issues: 87\n\nCreated: 2025-11-04T10:38:17Z\n\nPushed: 2026-06-10T14:42:47Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# pg_lake: Postgres for Iceberg and Data lakes\n\n`pg_lake` integrates Iceberg and data lake files into Postgres. With the `pg_lake` extensions, you can use Postgres as a stand-alone lakehouse system that supports transactions and fast queries on Iceberg tables, and can directly work with raw data files in object stores like S3.\n\nAt a high level, `pg_lake` lets you:\n\n- **Create and modify [Iceberg](https://iceberg.apache.org/)** tables directly from PostgreSQL, with full transactional guarantees and query them from other engines\n- **Query and import data files in object storage** in [Parquet](https://parquet.apache.org/), CSV, JSON, and Iceberg format\n- **Export query results back to object storage** in Parquet, CSV, or JSON formats using COPY commands\n- **Read geospatial formats** supported by GDAL, such as GeoJSON and Shapefiles\n- **Use the built-in [map type](./pg_map/README.md)** for semi-structured or key–value data \n- **Combine heap, Iceberg, and external Parquet/CSV/JSON** files in the same SQL queries and modifications — all with full transactional guarantees and no SQL limitations \n- **Infer table columns and types** from external data sources such as Iceberg, Parquet, JSON, and CSV files\n- **Leverage DuckDB’s query engine** underneath for fast execution without leaving Postgres \n\n## Setting up `pg_lake`\n\nThere are two ways to set up `pg_lake`: \n- **Using Docker**, for an easy, ready-to-run test environment. \n- **Building from source**, for a manual setup or development use. \n\nBoth approaches include the PostgreSQL extensions, the `pgduck_server` application and setting up S3-compatible storage.\n\n### Using Docker\n\nFollow the [Docker README](./docker/README.md) to set up and run `pg_lake` with Docker.\n\n### Building from source\n\nOnce you’ve [built and installed the required components](./docs/building-from-source.md), you can initialize `pg_lake` inside Postgres.\n\n#### Creating t"},{"ref":"P21","kind":"page","title":"Snowflake-Labs/sfguide-intro-to-online-feature-store-in-snowflake repository metadata","date":"2026-06-11T04:07:56.415888+00:00","date_source":null,"source_url":"https://github.com/Snowflake-Labs/sfguide-intro-to-online-feature-store-in-snowflake","signal_url":null,"signal_json_url":null,"text":"# Snowflake-Labs/sfguide-intro-to-online-feature-store-in-snowflake\n\nLanguage: Jupyter Notebook\n\nLicense: Apache-2.0\n\nStars: 0\n\nForks: 3\n\nOpen issues: 0\n\nCreated: 2025-11-04T21:04:45Z\n\nPushed: 2025-11-29T00:50:01Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# Introduction to Online Feature Store in Snowflake\n\n## Overview\nThrough this quickstart guide, you will explore the Snowflake Online Feature Store and learn how to build an end-to-end machine learning workflow. You will register entities and feature views, perform feature engineering, and use both online and offline stores for real-time inference to predict NYC taxi trip durations.\n\n## Step-By-Step Guide\nFor prerequisites, setup, step-by-step guide and instructions, please refer to the [QuickStart Guide](https://www.snowflake.com/en/developers/guides/intro-to-online-feature-store-in-snowflake/)."},{"ref":"P22","kind":"page","title":"Snowflake-Labs/sfguide-declarative-pipelines-dynamic-tables repository metadata","date":"2026-06-11T04:07:56.266621+00:00","date_source":null,"source_url":"https://github.com/Snowflake-Labs/sfguide-declarative-pipelines-dynamic-tables","signal_url":null,"signal_json_url":null,"text":"# Snowflake-Labs/sfguide-declarative-pipelines-dynamic-tables\n\nLanguage: Python\n\nStars: 26\n\nForks: 29\n\nOpen issues: 0\n\nCreated: 2025-11-05T03:30:21Z\n\nPushed: 2026-05-27T10:57:41Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# Build Autonomous SQL Pipelines with Cortex Code & Dynamic Tables\n\nThis lab demonstrates building a declarative data pipeline using Snowflake Dynamic Tables — driven entirely by natural language prompts to **Cortex Code** inside Snowsight Workspaces.\n\nInstead of manually writing SQL, you describe your pipeline intent to Cortex Code and it generates, validates, and executes the SQL for you.\n\n## Prerequisites\n\n- Snowflake account with ACCOUNTADMIN access ([free trial](https://signup.snowflake.com/developers))\n- Cortex Code enabled ([setup guide](https://docs.snowflake.com/en/user-guide/cortex-code))\n- Basic familiarity with data engineering concepts\n\n## How It Works\n\nEach SQL file in this repo follows a **prompt-first pattern**:\n\n```sql\n/*\n================================================================================\nCORTEX CODE PROMPT\n================================================================================\n<natural language prompt to give to Cortex Code>\n================================================================================\nEXPECTED OUTPUT\n<description of what CoCo should produce>\n================================================================================\n*/\n\n-- The expected SQL follows below...\n```\n\n**Workflow:**\n1. Create a Snowsight Workspace from this repository\n2. Open the Cortex Code panel (Cmd+L)\n3. Open a SQL file — copy the prompt at the top into CoCo\n4. Review CoCo's generated SQL against the expected output below\n5. Execute when satisfied\n\nThe SQL files remain fully runnable on their own for anyone who prefers the traditional approach.\n\n## Files\n\n| File | Purpose | CoCo Approach |\n|:-----|:--------|:--------------|\n| `00_setup_environment.sql` | Role, DB, warehouse, tables, data load | Direct execution |\n| `01_dynamic_tables.sql` | 3-tier pipeline (5 dynamic tables) | Generate-then-confirm |\n| `02_sproc.sql` | Stored procedure for synthetic test data | Generate-then-confirm |\n| `03_incremental_ref"},{"ref":"P23","kind":"page","title":"Snowflake-Labs/getting-started-with-dynamic-tables repository metadata","date":"2026-06-11T04:07:56.260129+00:00","date_source":null,"source_url":"https://github.com/Snowflake-Labs/getting-started-with-dynamic-tables","signal_url":null,"signal_json_url":null,"text":"# Snowflake-Labs/getting-started-with-dynamic-tables\n\nStars: 0\n\nForks: 0\n\nOpen issues: 0\n\nCreated: 2025-11-04T15:31:22Z\n\nPushed: 2025-11-04T15:31:22Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME: none published or not readable through the GitHub API."},{"ref":"P24","kind":"page","title":"Snowflake-Labs/sfguide-getting-started-with-snowflake-managed-mcp-and-bedrock-agentcore repository metadata","date":"2026-06-11T04:07:56.258225+00:00","date_source":null,"source_url":"https://github.com/Snowflake-Labs/sfguide-getting-started-with-snowflake-managed-mcp-and-bedrock-agentcore","signal_url":null,"signal_json_url":null,"text":"# Snowflake-Labs/sfguide-getting-started-with-snowflake-managed-mcp-and-bedrock-agentcore\n\nLicense: Apache-2.0\n\nStars: 2\n\nForks: 0\n\nOpen issues: 0\n\nCreated: 2025-11-05T22:30:35Z\n\nPushed: 2025-11-06T18:52:04Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# Getting Started with Snowflake Managed MCP and Bedrock AgentCore\n\n## Overview\n\nThis project utilizes the Snowflake Managed MCP Server to analyze pokemon site data with Bedrock AgentCore. \n\nPlease refer to the Guid for complete instructions"},{"ref":"P25","kind":"page","title":"Snowflake-Labs/awesome-custom-cortex-agents-rest-api-react-app repository metadata","date":"2026-06-11T04:07:55.908946+00:00","date_source":null,"source_url":"https://github.com/Snowflake-Labs/awesome-custom-cortex-agents-rest-api-react-app","signal_url":null,"signal_json_url":null,"text":"# Snowflake-Labs/awesome-custom-cortex-agents-rest-api-react-app\n\nLanguage: TypeScript\n\nLicense: Apache-2.0\n\nStars: 20\n\nForks: 11\n\nOpen issues: 0\n\nCreated: 2025-11-06T03:08:17Z\n\nPushed: 2025-11-17T16:27:23Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# Overview\n\nThis guide provides the instructions to build custom application for the data agents you have already built in your Snowflake account. The LLM orchestration, planning, thinking, deep reasoning, and execution of SQL queries, etc. (similar to [ai.snowflake.com](ai.snowflake.com)) is powered by [Snowflake Cortex Agents](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents) via the [REST API](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents-rest-api) and the interface is built with TypeScript and Material-UI.\n\nUse this as a starter project or template and extend or customize it. Also note that agents can make mistakes, so double-check responses.\n\n## Table of Contents\n- [Prerequisites](#prerequisites)\n- [Setup Steps](#setup-steps)\n- [1. Clone Repo](#1-clone-repo)\n- [2. Install Dependencies](#2-install-dependencies)\n- [3. Configure Environment Variables](#3-configure-environment-variables)\n- [Launch Application](#launch-application)\n- [Demo](#demo)\n- [Usage](#usage)\n- [Implemented Features](#implemented-features)\n- [Future Enhancements](#future-enhancements)\n- [Common Issues](#common-issues)\n- [Optimized Build](#optimized-build)\n- [Deploy To Snowpark Container Services](#deploy-to-snowpark-container-services)\n- [Questions](#questions)\n\n---\n\n## Prerequisites\n\n- **Node.js** >= 18.0.0\n- **npm** >= 9.0.0\n- **Snowflake Account** \n- With ACCOUNTADMIN role \n- Personal Access Token (PAT) created for authentication\n- Access to Snowflake Intelligence and at least one Agent\n\nNOTE: If you have not created an agent or do not have access to one, follow this [step-by-step guide](https://quickstarts.snowflake.com/guide/getting-started-with-snowflake-intelligence/index.html#0). You can then use the same account and interact with that agent in this application.\n\n## Setup Steps\n\n### 1. Clone Repo\n\nClone this repository to get the required files.\n\n* Folders\n- `src/` \n- `server/"},{"ref":"P26","kind":"page","title":"Snowflake-Labs/sfguide-customer-journey-analytics-with-sequent repository metadata","date":"2026-06-11T04:07:55.619007+00:00","date_source":null,"source_url":"https://github.com/Snowflake-Labs/sfguide-customer-journey-analytics-with-sequent","signal_url":null,"signal_json_url":null,"text":"# Snowflake-Labs/sfguide-customer-journey-analytics-with-sequent\n\nLanguage: Python\n\nLicense: Apache-2.0\n\nStars: 1\n\nForks: 4\n\nOpen issues: 0\n\nCreated: 2025-11-12T20:14:43Z\n\nPushed: 2025-12-09T03:01:37Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# Customer Journey Analytics with Sequent™\n\n## Overview\n\nSequent™ native application allows users to easily and visually perform and deep dive into Path Analysis, Attribution Analysis, Association Analysis, Pattern Mining, Behavioral Segmentation and Predictive Modeling by simply specifying a few parameters in drop-down menus. Leveraging advanced techniques, Sequent™ intuitively and visually helps identify touchpoints influencing customer (or machine) behaviours, targets them to create segments, performs cross-population behavioural comparisons, computes rule-based and ML-driven attribution models to understand the contribution of each event preceding a specific outcome, conducts association analysis to uncover hidden patterns and relationships between events, discovers frequent sequential patterns and behavioral signatures through advanced pattern mining, and enables sophisticated behavioral segmentation to group customers based on their journey patterns and characteristics. Sequent™ also harnesses the interpretive and generative power of LLMs thanks to Snowflake AISQL to explain journeys, attribution models, association rules, pattern insights and derive insights (summarize and analyze results, describe behaviors and even suggest actions!)\n\nVisualizing and identifying paths can itself be actionable and often uncovers an area of interest for additional analysis. First, the picture revealed by path analysis can be further enriched with attribution analysis, association analysis, pattern mining, and behavioral segmentation. Attribution helps quantify the contribution of individual touchpoints to a defined outcome, association analysis uncovers relationships between events that frequently occur together, pattern mining discovers frequent sequential behaviors and hidden temporal dependencies, and behavioral segmentation groups customers into meaningful clusters based on their journey characteristics and patterns. "},{"ref":"P27","kind":"page","title":"Snowflake-Labs/sfguide-Build-an-AI-Assistant-for-FSI-with-AISQL-and-Snowflake-Intelligence repository metadata","date":"2026-06-11T04:07:55.564553+00:00","date_source":null,"source_url":"https://github.com/Snowflake-Labs/sfguide-Build-an-AI-Assistant-for-FSI-with-AISQL-and-Snowflake-Intelligence","signal_url":null,"signal_json_url":null,"text":"# Snowflake-Labs/sfguide-Build-an-AI-Assistant-for-FSI-with-AISQL-and-Snowflake-Intelligence\n\nLanguage: Jupyter Notebook\n\nLicense: Apache-2.0\n\nStars: 0\n\nForks: 0\n\nOpen issues: 0\n\nCreated: 2025-11-27T13:45:44Z\n\nPushed: 2025-12-13T00:05:36Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# Building an AI Assistant for FSI using AISQL and Snowflake Intelligence\n\n## Overview\n\nBuild a production-ready AI assistant for financial services using **Snowflake Cortex AI**, **Snowflake Intelligence**, and **Document AI**. \n\n**No downloads required!** Deploy directly from GitHub using Snowflake's Git integration.\n\n### What You'll Build\n\n- **Unstructured Data Processing**: Extract insights from PDFs, images, and audio using Document AI\n- **Structured Data Processing**: Build ML models for stock prediction using Snowflake ML\n- **Agent Tools**: Create semantic search services and natural language query interfaces\n- **Applications**: Deploy intelligent agents with Snowflake Intelligence and Streamlit\n\n## Use Case\n\nBuild a Stock Selection Agent that processes and analyzes data from diverse sources:\n\n- **30 Analyst Reports** from 6 research firms with ratings and price targets\n- **92 Earnings Call Transcripts** from 11 companies with sentiment analysis\n- **11 Financial Infographics** with extracted KPIs\n- **950 Analyst Emails** with AI-extracted tickers and ratings\n- **6,420 Stock Price Data Points** for predictive modeling\n- **22 Annual Reports** with comprehensive financial statements\n- **4 Audio Files** transcribed with speaker identification\n\n## Quick Start (15-20 minutes)\n\n### Prerequisites\n\n- Snowflake account (free trial works for most features)\n- ACCOUNTADMIN role access\n- No downloads or CLI tools needed\n\n> **⚠️ Note for Trial Accounts:** The **Web Search** feature requires External Access Integration, which is not enabled by default on trial accounts. If you need this feature, contact your Snowflake representative to have it enabled. All other features work without this.\n\n### Step 1: Setup Git Integration\n\nOpen a SQL Worksheet in Snowflake and run:\n\n```sql\nUSE ROLE ACCOUNTADMIN;\n\n-- Create separate database for Git repos (persists across deployments)\nCREATE DATABA"},{"ref":"P28","kind":"page","title":"Snowflake-Labs/sfguide-getting-started-with-predictive-maintenance repository metadata","date":"2026-06-11T04:07:55.475902+00:00","date_source":null,"source_url":"https://github.com/Snowflake-Labs/sfguide-getting-started-with-predictive-maintenance","signal_url":null,"signal_json_url":null,"text":"# Snowflake-Labs/sfguide-getting-started-with-predictive-maintenance\n\nLanguage: Python\n\nLicense: MIT\n\nStars: 0\n\nForks: 2\n\nOpen issues: 0\n\nCreated: 2025-11-17T15:19:23Z\n\nPushed: 2026-03-18T14:52:09Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# Getting Started with Predictive Maintenance\n\nLearn how to build an end-to-end **predictive maintenance solution** using Snowflake's Data Cloud. This hands-on tutorial demonstrates how to construct a complete **data pipeline in Snowflake**—from ingesting raw sensor telemetry and maintenance logs to building analytics-ready tables and deploying an interactive **Streamlit application**. Finally, you will leverage **Snowflake Intelligence** to use natural language to discover topics ranging from predicting equipment failures, optimizing maintenance schedules, and reducing operational costs.\n\n## What You'll Build\n\nYou will build a predictive maintenance solution using Snowflake Intelligence, Streamlit in Snowflake, and an end-to-end Snowflake data pipeline. \n\n### 🤖 Natural Language Analytics with Snowflake Intelligence\nAsk questions about your data in plain English and get AI-powered insights:\n- Natural language queries against your predictive maintenance data\n- Conversational AI assistance for insights and recommendations\n- Automatic SQL generation and data visualization\n- Context-aware follow-up questions and analysis\n\n### 📊 Interactive Analytics Application\nDeploy a multi-page Streamlit dashboard that showcases:\n- Real-time monitoring of 18+ manufacturing assets across 3 facilities\n- AI-powered failure predictions and maintenance recommendations\n- Financial analysis and OEE (Overall Equipment Effectiveness) tracking\n- Interactive visualizations of production line status and asset health\n\n### 🏗️ Complete Data Pipeline in Snowflake\nBuild a data pipeline using Snowflake's medallion architecture:\n\n**Bronze Layer (Raw Data Ingestion)**\n- Ingest raw IoT sensor telemetry (temperature, vibration, pressure)\n- Load maintenance logs and work order data\n- Store equipment master data and specifications\n\n**Silver Layer (Curated & Conformed)**\n- Transform raw data into dimensional star schema\n- Build fact tables for sensor re"},{"ref":"E1","kind":"event","title":"Account Executive, Public Sector-SLED","date":"2026-06-10T20:37:37.669+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/snowflake/2bb5f38e-b7b0-4a45-93d1-29538e9b1b73","signal_url":"https://onlylabs.fyi/signals/ba53ef76-4847-481e-ba8d-167d3cc6c858","signal_json_url":"https://onlylabs.fyi/signals/ba53ef76-4847-481e-ba8d-167d3cc6c858/signal.json","text":"job_opened · Account Executive, Public Sector-SLED · signal_desk=hiring · occurred_at=2026-06-10T20:37:37.669+00:00 · url=https://jobs.ashbyhq.com/snowflake/2bb5f38e-b7b0-4a45-93d1-29538e9b1b73 · raw={\"location\":\"US-GA-Atlanta\",\"team\":\"Sales\",\"ats\":\"ashby\"}"},{"ref":"E2","kind":"event","title":"AI Data Engineering: New Smart Pipelines in Snowflake","date":"2026-06-10T19:01:00+00:00","date_source":"rss.item_date","source_url":"https://www.snowflake.com/content/snowflake-site/global/en/blog/ai-smart-pipelines-whats-new","signal_url":"https://onlylabs.fyi/signals/662530f1-d99b-4c40-ab89-0aa25f0ec7bd","signal_json_url":"https://onlylabs.fyi/signals/662530f1-d99b-4c40-ab89-0aa25f0ec7bd/signal.json","text":"post_published · AI Data Engineering: New Smart Pipelines in Snowflake · signal_desk=talking · occurred_at=2026-06-10T19:01:00+00:00 · url=https://www.snowflake.com/content/snowflake-site/global/en/blog/ai-smart-pipelines-whats-new · raw={\"excerpt\":\"Discover new AI tools for data engineering announced at Snowflake Summit 2026. Learn how Snowflake CoCo and smart pipelines accelerate your workflows.\"}"},{"ref":"E3","kind":"event","title":"Sr. Manager, Resource Delivery Manager","date":"2026-06-10T18:43:55.171+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/snowflake/a4dea157-34aa-4830-8894-4696f1dd843f","signal_url":"https://onlylabs.fyi/signals/df6b1b66-4270-41ae-a29a-c9b85bf50929","signal_json_url":"https://onlylabs.fyi/signals/df6b1b66-4270-41ae-a29a-c9b85bf50929/signal.json","text":"job_opened · Sr. Manager, Resource Delivery Manager · signal_desk=hiring · occurred_at=2026-06-10T18:43:55.171+00:00 · url=https://jobs.ashbyhq.com/snowflake/a4dea157-34aa-4830-8894-4696f1dd843f · raw={\"location\":\"US-IL-Chicago-MSO\",\"team\":\"Professional Services\",\"ats\":\"ashby\"}"},{"ref":"E4","kind":"event","title":"Solution Engineer","date":"2026-06-10T15:26:37.1+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/snowflake/1c9830cf-4309-49e5-abdd-0906f60ddbd2","signal_url":"https://onlylabs.fyi/signals/5ea72203-8234-4d23-bc65-97cb7bbf1ace","signal_json_url":"https://onlylabs.fyi/signals/5ea72203-8234-4d23-bc65-97cb7bbf1ace/signal.json","text":"job_opened · Solution Engineer · signal_desk=hiring · occurred_at=2026-06-10T15:26:37.1+00:00 · url=https://jobs.ashbyhq.com/snowflake/1c9830cf-4309-49e5-abdd-0906f60ddbd2 · raw={\"location\":\"SE-Stockholm-MSO\",\"team\":\"Solution Engineering\",\"ats\":\"ashby\"}"},{"ref":"E5","kind":"event","title":"Associate Solution Engineer","date":"2026-06-10T15:26:10.809+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/snowflake/c0d03ec9-2bfa-497b-b6a6-e045d1801206","signal_url":"https://onlylabs.fyi/signals/e62c6614-b61f-4e86-adb3-9f1cb6d80733","signal_json_url":"https://onlylabs.fyi/signals/e62c6614-b61f-4e86-adb3-9f1cb6d80733/signal.json","text":"job_opened · Associate Solution Engineer · signal_desk=hiring · occurred_at=2026-06-10T15:26:10.809+00:00 · url=https://jobs.ashbyhq.com/snowflake/c0d03ec9-2bfa-497b-b6a6-e045d1801206 · raw={\"location\":\"SE-Stockholm-MSO\",\"team\":\"Solution Engineering\",\"ats\":\"ashby\"}"},{"ref":"E6","kind":"event","title":"Hybrid Tables Just Got Up to 8x Faster","date":"2026-06-10T15:00:00+00:00","date_source":"rss.item_date","source_url":"https://www.snowflake.com/content/snowflake-site/global/en/blog/hybrid-tables-performance-improvements","signal_url":"https://onlylabs.fyi/signals/22db6ab1-65b7-45fc-be1f-788b573cbacb","signal_json_url":"https://onlylabs.fyi/signals/22db6ab1-65b7-45fc-be1f-788b573cbacb/signal.json","text":"post_published · Hybrid Tables Just Got Up to 8x Faster · signal_desk=talking · occurred_at=2026-06-10T15:00:00+00:00 · url=https://www.snowflake.com/content/snowflake-site/global/en/blog/hybrid-tables-performance-improvements · raw={\"excerpt\":\"Snowflake Hybrid Tables now attain up to 8x higher throughput, 10x faster batch performance and up to 40% cost savings (based on internal benchmarks) with no code changes required. See how teams are running transactional and analytical workloads together on Snowflake.\"}"},{"ref":"E7","kind":"event","title":"Applied AI Engineer","date":"2026-06-10T08:14:26.849+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/snowflake/467250b0-43bc-47aa-91d5-6312687e4097","signal_url":"https://onlylabs.fyi/signals/379877f2-0593-4fe2-922a-a14d22f706e5","signal_json_url":"https://onlylabs.fyi/signals/379877f2-0593-4fe2-922a-a14d22f706e5/signal.json","text":"job_opened · Applied AI Engineer · signal_desk=hiring · occurred_at=2026-06-10T08:14:26.849+00:00 · url=https://jobs.ashbyhq.com/snowflake/467250b0-43bc-47aa-91d5-6312687e4097 · raw={\"location\":\"PL-Warsaw\",\"team\":\"Engineering\",\"ats\":\"ashby\"}"},{"ref":"E8","kind":"event","title":"Partner Development Manager - ASEAN (GSI)","date":"2026-06-10T03:17:46.451+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/snowflake/24083626-d2b0-4c90-94ad-62b535f76b47","signal_url":"https://onlylabs.fyi/signals/ee1cfe82-1dbb-470e-830b-89c210b3d8d6","signal_json_url":"https://onlylabs.fyi/signals/ee1cfe82-1dbb-470e-830b-89c210b3d8d6/signal.json","text":"job_opened · Partner Development Manager - ASEAN (GSI) · signal_desk=hiring · occurred_at=2026-06-10T03:17:46.451+00:00 · url=https://jobs.ashbyhq.com/snowflake/24083626-d2b0-4c90-94ad-62b535f76b47 · raw={\"location\":\"SG-Singapore\",\"team\":\"Alliances and Channels\",\"ats\":\"ashby\"}"},{"ref":"E9","kind":"event","title":"Announcing Claude Fable 5 on Snowflake Cortex AI","date":"2026-06-09T19:39:32+00:00","date_source":"rss.item_date","source_url":"https://www.snowflake.com/content/snowflake-site/global/en/blog/claude-fable-5-snowflake-cortex-ai","signal_url":"https://onlylabs.fyi/signals/80dee8f6-2990-444f-bccd-d79ef5c0a788","signal_json_url":"https://onlylabs.fyi/signals/80dee8f6-2990-444f-bccd-d79ef5c0a788/signal.json","text":"post_published · Announcing Claude Fable 5 on Snowflake Cortex AI · signal_desk=talking · occurred_at=2026-06-09T19:39:32+00:00 · url=https://www.snowflake.com/content/snowflake-site/global/en/blog/claude-fable-5-snowflake-cortex-ai · raw={\"excerpt\":\"Claude Fable 5 is now available on Snowflake Cortex AI, bringing advanced reasoning and agentic capabilities with Snowflake’s secure governed AI platform\"}"},{"ref":"E10","kind":"event","title":"Anti-Abuse Senior Software Engineer, Product Security","date":"2026-06-09T18:48:30.058+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/snowflake/bcb00e62-34db-41b4-a044-07f4438ea60a","signal_url":"https://onlylabs.fyi/signals/3d5da1e9-94e9-4e8d-b900-844b3964e35a","signal_json_url":"https://onlylabs.fyi/signals/3d5da1e9-94e9-4e8d-b900-844b3964e35a/signal.json","text":"job_opened · Anti-Abuse Senior Software Engineer, Product Security · signal_desk=hiring · occurred_at=2026-06-09T18:48:30.058+00:00 · url=https://jobs.ashbyhq.com/snowflake/bcb00e62-34db-41b4-a044-07f4438ea60a · raw={\"location\":\"US-WA-Bellevue\",\"team\":\"Engineering\",\"ats\":\"ashby\"}"},{"ref":"E11","kind":"event","title":"Sr. Technical Delivery Director","date":"2026-06-09T18:01:18.844+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/snowflake/ce89d7db-4c16-4569-98b5-f66f976e99aa","signal_url":"https://onlylabs.fyi/signals/e98b3fd5-be33-4c1e-8b6e-05cb1bd08277","signal_json_url":"https://onlylabs.fyi/signals/e98b3fd5-be33-4c1e-8b6e-05cb1bd08277/signal.json","text":"job_opened · Sr. Technical Delivery Director · signal_desk=hiring · occurred_at=2026-06-09T18:01:18.844+00:00 · url=https://jobs.ashbyhq.com/snowflake/ce89d7db-4c16-4569-98b5-f66f976e99aa · raw={\"location\":\"US-CA-Menlo Park\",\"team\":\"Professional Services\",\"ats\":\"ashby\"}"},{"ref":"E12","kind":"event","title":"Principal Product Manager - Zero-Copy Integrations","date":"2026-06-09T17:50:12.657+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/snowflake/0131b6dc-50b8-48bc-856a-b797554b484d","signal_url":"https://onlylabs.fyi/signals/2d494c0f-a740-4f5a-91bd-44e8dcd874a1","signal_json_url":"https://onlylabs.fyi/signals/2d494c0f-a740-4f5a-91bd-44e8dcd874a1/signal.json","text":"job_opened · Principal Product Manager - Zero-Copy Integrations · signal_desk=hiring · occurred_at=2026-06-09T17:50:12.657+00:00 · url=https://jobs.ashbyhq.com/snowflake/0131b6dc-50b8-48bc-856a-b797554b484d · raw={\"location\":\"US-WA-Bellevue\",\"team\":\"Product Management\",\"ats\":\"ashby\"}"},{"ref":"E13","kind":"event","title":"Senior Practice Manager, Healthcare & Life Science","date":"2026-06-09T12:11:23.158+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/snowflake/0cd49ae4-c65f-4321-802d-2cf30ba4a376","signal_url":"https://onlylabs.fyi/signals/9f924dc0-2d4b-4ca2-9b26-b262150a47d5","signal_json_url":"https://onlylabs.fyi/signals/9f924dc0-2d4b-4ca2-9b26-b262150a47d5/signal.json","text":"job_opened · Senior Practice Manager, Healthcare & Life Science · signal_desk=hiring · occurred_at=2026-06-09T12:11:23.158+00:00 · url=https://jobs.ashbyhq.com/snowflake/0cd49ae4-c65f-4321-802d-2cf30ba4a376 · raw={\"location\":\"US-NY-New York City-Remote\",\"team\":\"Professional Services\",\"ats\":\"ashby\"}"},{"ref":"E14","kind":"event","title":"SnowCAT Technical Principal","date":"2026-06-09T02:07:51.241+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/snowflake/9c44b1c2-f3f9-4e64-9540-bbc06dcbefda","signal_url":"https://onlylabs.fyi/signals/ef3869b9-0ad6-4b5c-ad07-a2791b93eadd","signal_json_url":"https://onlylabs.fyi/signals/ef3869b9-0ad6-4b5c-ad07-a2791b93eadd/signal.json","text":"job_opened · SnowCAT Technical Principal · signal_desk=hiring · occurred_at=2026-06-09T02:07:51.241+00:00 · url=https://jobs.ashbyhq.com/snowflake/9c44b1c2-f3f9-4e64-9540-bbc06dcbefda · raw={\"location\":\"US-USA-Remote\",\"team\":\"Product Management\",\"ats\":\"ashby\"}"},{"ref":"E15","kind":"event","title":"Senior Solution Engineer - Federal - (Military Health or DIB)","date":"2026-06-08T20:22:39.092+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/snowflake/8cb677ad-7b2b-4167-b53f-e64af4f8cafc","signal_url":"https://onlylabs.fyi/signals/8a23a0da-b97c-45ea-a1d8-5e3b0db84964","signal_json_url":"https://onlylabs.fyi/signals/8a23a0da-b97c-45ea-a1d8-5e3b0db84964/signal.json","text":"job_opened · Senior Solution Engineer - Federal - (Military Health or DIB) · signal_desk=hiring · occurred_at=2026-06-08T20:22:39.092+00:00 · url=https://jobs.ashbyhq.com/snowflake/8cb677ad-7b2b-4167-b53f-e64af4f8cafc · raw={\"location\":\"US-VA-Remote\",\"team\":\"Solution Engineering\",\"ats\":\"ashby\"}"},{"ref":"E16","kind":"event","title":"Senior Cloud Support Engineer - Security","date":"2026-06-08T18:19:39.621+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/snowflake/586856a2-06fb-4811-8f71-8a596641b414","signal_url":"https://onlylabs.fyi/signals/9533e89e-107c-4702-b088-853494a9485d","signal_json_url":"https://onlylabs.fyi/signals/9533e89e-107c-4702-b088-853494a9485d/signal.json","text":"job_opened · Senior Cloud Support Engineer - Security · signal_desk=hiring · occurred_at=2026-06-08T18:19:39.621+00:00 · url=https://jobs.ashbyhq.com/snowflake/586856a2-06fb-4811-8f71-8a596641b414 · raw={\"location\":\"US-GA-Atlanta\",\"team\":\"Global Support\",\"ats\":\"ashby\"}"},{"ref":"E17","kind":"event","title":"Deploy Snowpark Python via Snowflake CoCo","date":"2026-06-08T16:28:00+00:00","date_source":"rss.item_date","source_url":"https://www.snowflake.com/content/snowflake-site/global/en/blog/deploy-snowpark-python-snowflake-coco","signal_url":"https://onlylabs.fyi/signals/6b3d37be-bf9e-4038-b625-2c3a67d199f2","signal_json_url":"https://onlylabs.fyi/signals/6b3d37be-bf9e-4038-b625-2c3a67d199f2/signal.json","text":"post_published · Deploy Snowpark Python via Snowflake CoCo · signal_desk=talking · occurred_at=2026-06-08T16:28:00+00:00 · url=https://www.snowflake.com/content/snowflake-site/global/en/blog/deploy-snowpark-python-snowflake-coco · raw={\"excerpt\":\"Discover how Snowflake CoCo helps developers deploy Snowpark Python pipelines to production with a single prompt. 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Engineer","date":"2026-06-08T12:35:55.087+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/snowflake/850505b0-50f8-405a-868e-b9957453047b","signal_url":"https://onlylabs.fyi/signals/ad48c084-fa80-43a5-abe9-c890fc7ce7b3","signal_json_url":"https://onlylabs.fyi/signals/ad48c084-fa80-43a5-abe9-c890fc7ce7b3/signal.json","text":"job_opened · Senior Solution Engineer · signal_desk=hiring · occurred_at=2026-06-08T12:35:55.087+00:00 · url=https://jobs.ashbyhq.com/snowflake/850505b0-50f8-405a-868e-b9957453047b · raw={\"location\":\"US-GA-Atlanta\",\"team\":\"Solution Engineering\",\"ats\":\"ashby\"}"},{"ref":"E23","kind":"event","title":"Technical Architect - Migrations","date":"2026-06-08T07:33:06.318+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/snowflake/5cf06916-aef0-4fe5-9925-2410389be9d7","signal_url":"https://onlylabs.fyi/signals/e5e0e68f-ad0c-4c4d-acec-9f740a247308","signal_json_url":"https://onlylabs.fyi/signals/e5e0e68f-ad0c-4c4d-acec-9f740a247308/signal.json","text":"job_opened · Technical Architect - Migrations · signal_desk=hiring · occurred_at=2026-06-08T07:33:06.318+00:00 · url=https://jobs.ashbyhq.com/snowflake/5cf06916-aef0-4fe5-9925-2410389be9d7 · raw={\"location\":\"NL-Netherlands-Remote\",\"team\":\"Professional Services\",\"ats\":\"ashby\"}"},{"ref":"E24","kind":"event","title":"Senior Product 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AI","date":"2026-06-05T14:29:30.819+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/snowflake/9588a645-9122-45d8-a09f-f29196f80d4d","signal_url":"https://onlylabs.fyi/signals/cd74e577-a4b3-4973-9a56-144023057cd6","signal_json_url":"https://onlylabs.fyi/signals/cd74e577-a4b3-4973-9a56-144023057cd6/signal.json","text":"job_opened · Senior Manager, Finance Analytics & AI · signal_desk=hiring · occurred_at=2026-06-05T14:29:30.819+00:00 · url=https://jobs.ashbyhq.com/snowflake/9588a645-9122-45d8-a09f-f29196f80d4d · raw={\"location\":\"US-CA-Menlo Park\",\"team\":\"Data Analytics and AI\",\"ats\":\"ashby\"}"},{"ref":"E32","kind":"event","title":"Data Engineer","date":"2026-06-05T08:09:36.053+00:00","date_source":"source","source_url":"https://jobs.ashbyhq.com/snowflake/e386e099-80b8-47af-bb7a-e3e8a1571d5f","signal_url":"https://onlylabs.fyi/signals/a9ca7ee5-d451-4892-88d6-4bb9ef97ecf4","signal_json_url":"https://onlylabs.fyi/signals/a9ca7ee5-d451-4892-88d6-4bb9ef97ecf4/signal.json","text":"job_opened · Data Engineer · signal_desk=hiring · occurred_at=2026-06-05T08:09:36.053+00:00 · url=https://jobs.ashbyhq.com/snowflake/e386e099-80b8-47af-bb7a-e3e8a1571d5f · raw={\"location\":\"IN-Pune\",\"team\":\"Data Analytics and AI\",\"ats\":\"ashby\"}"},{"ref":"E33","kind":"event","title":"Senior Software Engineer - Internal Observability","date":"2026-06-05T04:12:39.132+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/snowflake/a438f51d-cbca-4751-88c3-5de326833bba","signal_url":"https://onlylabs.fyi/signals/6fd7f6ed-cc24-417e-b7c0-8f46b2f97b1d","signal_json_url":"https://onlylabs.fyi/signals/6fd7f6ed-cc24-417e-b7c0-8f46b2f97b1d/signal.json","text":"job_opened · Senior Software Engineer - Internal Observability · signal_desk=hiring · occurred_at=2026-06-05T04:12:39.132+00:00 · url=https://jobs.ashbyhq.com/snowflake/a438f51d-cbca-4751-88c3-5de326833bba · raw={\"location\":\"US-CA-Menlo Park\",\"team\":\"Engineering\",\"ats\":\"ashby\"}"},{"ref":"E34","kind":"event","title":"Senior Corporate Counsel 1 - Intellectual 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Manufacturing","date":"2026-06-04T20:16:06.194+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/snowflake/8f56bbf5-4d87-49a4-993a-6e5063f96cc2","signal_url":"https://onlylabs.fyi/signals/dad6f8fb-fedf-4749-83c3-afb046dd7a4f","signal_json_url":"https://onlylabs.fyi/signals/dad6f8fb-fedf-4749-83c3-afb046dd7a4f/signal.json","text":"job_opened · Account Executive, Majors, Manufacturing · signal_desk=hiring · occurred_at=2026-06-04T20:16:06.194+00:00 · url=https://jobs.ashbyhq.com/snowflake/8f56bbf5-4d87-49a4-993a-6e5063f96cc2 · raw={\"location\":\"US-CA-Menlo Park\",\"team\":\"Sales\",\"ats\":\"ashby\"}"},{"ref":"E36","kind":"event","title":"OpenAI and Snowflake: The Future of Business-Native AI","date":"2026-06-04T16:00:00+00:00","date_source":"rss.item_date","source_url":"https://www.snowflake.com/content/snowflake-site/global/en/blog/openai-snowflake-business-native-ai","signal_url":"https://onlylabs.fyi/signals/659b23d2-4f19-4268-bc0e-3b6818446b88","signal_json_url":"https://onlylabs.fyi/signals/659b23d2-4f19-4268-bc0e-3b6818446b88/signal.json","text":"post_published · OpenAI and Snowflake: The Future of Business-Native AI · signal_desk=talking · occurred_at=2026-06-04T16:00:00+00:00 · url=https://www.snowflake.com/content/snowflake-site/global/en/blog/openai-snowflake-business-native-ai · raw={\"excerpt\":\"Learn how OpenAI and Snowflake are bringing frontier intelligence directly to enterprise data securely, at scale and without compromise.\"}"},{"ref":"E37","kind":"event","title":"Senior Solution Engineer","date":"2026-06-04T14:53:26.436+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/snowflake/f621dbb9-9398-45e4-bcd1-3eb362224296","signal_url":"https://onlylabs.fyi/signals/eaafc885-43d7-4dc7-a0f9-929049d437b4","signal_json_url":"https://onlylabs.fyi/signals/eaafc885-43d7-4dc7-a0f9-929049d437b4/signal.json","text":"job_opened · Senior Solution Engineer · signal_desk=hiring · occurred_at=2026-06-04T14:53:26.436+00:00 · url=https://jobs.ashbyhq.com/snowflake/f621dbb9-9398-45e4-bcd1-3eb362224296 · raw={\"location\":\"AE-Dubai\",\"team\":\"Solution Engineering\",\"ats\":\"ashby\"}"},{"ref":"E38","kind":"event","title":"Account Executive, Enterprise Acquistion","date":"2026-06-04T14:20:40.138+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/snowflake/cfefa776-46f0-4a8c-81c8-85f6496925ea","signal_url":"https://onlylabs.fyi/signals/b79bb431-4d60-4e84-91d3-6d288609935a","signal_json_url":"https://onlylabs.fyi/signals/b79bb431-4d60-4e84-91d3-6d288609935a/signal.json","text":"job_opened · Account Executive, Enterprise Acquistion · signal_desk=hiring · occurred_at=2026-06-04T14:20:40.138+00:00 · url=https://jobs.ashbyhq.com/snowflake/cfefa776-46f0-4a8c-81c8-85f6496925ea · raw={\"location\":\"US-CA-Menlo Park\",\"team\":\"Sales\",\"ats\":\"ashby\"}"},{"ref":"E39","kind":"event","title":"Senior Solution Engineer, Media & Entertainment","date":"2026-06-04T12:35:51.739+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/snowflake/1b7846a6-c18c-4bb9-a1c0-b4efb75c1b37","signal_url":"https://onlylabs.fyi/signals/448d39be-6fb5-48d6-b398-4d5d0e7d099f","signal_json_url":"https://onlylabs.fyi/signals/448d39be-6fb5-48d6-b398-4d5d0e7d099f/signal.json","text":"job_opened · Senior Solution Engineer, Media & Entertainment · signal_desk=hiring · occurred_at=2026-06-04T12:35:51.739+00:00 · url=https://jobs.ashbyhq.com/snowflake/1b7846a6-c18c-4bb9-a1c0-b4efb75c1b37 · raw={\"location\":\"US-CA-Bay Area-Remote\",\"team\":\"Solution Engineering\",\"ats\":\"ashby\"}"},{"ref":"E40","kind":"event","title":"Senior Manager, Solution Engineering 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Architect","date":"2026-06-03T23:12:35.859+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/snowflake/ccc6c304-07da-46cf-95da-877eed922e11","signal_url":"https://onlylabs.fyi/signals/ab0a1c22-2f0a-40aa-b813-8d5a7f986ab3","signal_json_url":"https://onlylabs.fyi/signals/ab0a1c22-2f0a-40aa-b813-8d5a7f986ab3/signal.json","text":"job_opened · Technical Architect · signal_desk=hiring · occurred_at=2026-06-03T23:12:35.859+00:00 · url=https://jobs.ashbyhq.com/snowflake/ccc6c304-07da-46cf-95da-877eed922e11 · raw={\"location\":\"JP-Tokyo\",\"team\":\"Professional Services\",\"ats\":\"ashby\"}"},{"ref":"E43","kind":"event","title":"Senior Solution Engineer","date":"2026-06-03T21:40:56.243+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/snowflake/3dbc6708-52dc-4b8d-b37f-6a8d81463b12","signal_url":"https://onlylabs.fyi/signals/82525149-0399-400a-9659-0fd9df0f6683","signal_json_url":"https://onlylabs.fyi/signals/82525149-0399-400a-9659-0fd9df0f6683/signal.json","text":"job_opened · Senior Solution Engineer · signal_desk=hiring · occurred_at=2026-06-03T21:40:56.243+00:00 · url=https://jobs.ashbyhq.com/snowflake/3dbc6708-52dc-4b8d-b37f-6a8d81463b12 · raw={\"location\":\"US-MN-Remote\",\"team\":\"Solution Engineering\",\"ats\":\"ashby\"}"},{"ref":"E44","kind":"event","title":"Senior Solution Engineer - Observe by Snowflake","date":"2026-06-03T18:15:30.898+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/snowflake/8850cf03-3370-47d7-ae7f-f732f4100fa0","signal_url":"https://onlylabs.fyi/signals/54e815c6-0453-4c1a-b1eb-b173dda70919","signal_json_url":"https://onlylabs.fyi/signals/54e815c6-0453-4c1a-b1eb-b173dda70919/signal.json","text":"job_opened · Senior Solution Engineer - Observe by Snowflake · signal_desk=hiring · occurred_at=2026-06-03T18:15:30.898+00:00 · url=https://jobs.ashbyhq.com/snowflake/8850cf03-3370-47d7-ae7f-f732f4100fa0 · raw={\"location\":\"GB-London\",\"team\":\"Solution Engineering\",\"ats\":\"ashby\"}"},{"ref":"E45","kind":"event","title":"Principal Solution Engineer - Observe by Snowflake","date":"2026-06-03T18:15:26.098+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/snowflake/af11d12f-0420-4b61-9de2-305209456a18","signal_url":"https://onlylabs.fyi/signals/1c094637-0dd7-4830-a825-d946c3363c37","signal_json_url":"https://onlylabs.fyi/signals/1c094637-0dd7-4830-a825-d946c3363c37/signal.json","text":"job_opened · Principal Solution Engineer - Observe by Snowflake · signal_desk=hiring · occurred_at=2026-06-03T18:15:26.098+00:00 · url=https://jobs.ashbyhq.com/snowflake/af11d12f-0420-4b61-9de2-305209456a18 · raw={\"location\":\"GB-London\",\"team\":\"Solution Engineering\",\"ats\":\"ashby\"}"},{"ref":"E46","kind":"event","title":"Senior Software Engineer - Snowflake Postgres Control Plane ","date":"2026-06-03T17:38:01.194+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/snowflake/3acc2938-fbcb-4dcb-b512-05b62d03dcee","signal_url":"https://onlylabs.fyi/signals/ef0ebfda-de69-4766-b4d7-c0606084fcc3","signal_json_url":"https://onlylabs.fyi/signals/ef0ebfda-de69-4766-b4d7-c0606084fcc3/signal.json","text":"job_opened · Senior Software Engineer - Snowflake Postgres Control Plane  · signal_desk=hiring · occurred_at=2026-06-03T17:38:01.194+00:00 · url=https://jobs.ashbyhq.com/snowflake/3acc2938-fbcb-4dcb-b512-05b62d03dcee · raw={\"location\":\"US-CA-Menlo Park\",\"team\":\"Engineering\",\"ats\":\"ashby\"}"},{"ref":"E47","kind":"event","title":"Senior Software Engineer - Natsec","date":"2026-06-03T15:16:00.056+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/snowflake/9e56c0ed-b76e-49f1-b38d-a7e8b64bf7d9","signal_url":"https://onlylabs.fyi/signals/0db2d144-e936-4717-bc34-c0e44780fb9e","signal_json_url":"https://onlylabs.fyi/signals/0db2d144-e936-4717-bc34-c0e44780fb9e/signal.json","text":"job_opened · Senior Software Engineer - Natsec · signal_desk=hiring · occurred_at=2026-06-03T15:16:00.056+00:00 · url=https://jobs.ashbyhq.com/snowflake/9e56c0ed-b76e-49f1-b38d-a7e8b64bf7d9 · raw={\"location\":\"US-VA-McLean\",\"team\":\"Engineering\",\"ats\":\"ashby\"}"},{"ref":"E48","kind":"event","title":"Sales Development Representative","date":"2026-06-03T13:48:30.668+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/snowflake/3d6cd7d6-67a5-4e17-b6ae-58abdf8ba0df","signal_url":"https://onlylabs.fyi/signals/3a58a7ec-8489-4a5d-bd1d-e1353bd82d62","signal_json_url":"https://onlylabs.fyi/signals/3a58a7ec-8489-4a5d-bd1d-e1353bd82d62/signal.json","text":"job_opened · Sales Development Representative · signal_desk=hiring · occurred_at=2026-06-03T13:48:30.668+00:00 · url=https://jobs.ashbyhq.com/snowflake/3d6cd7d6-67a5-4e17-b6ae-58abdf8ba0df · raw={\"location\":\"CA-Ontario-Toronto\",\"team\":\"Sales Development\",\"ats\":\"ashby\"}"},{"ref":"E49","kind":"event","title":"Statutory Accountant / Associate Accountant","date":"2026-06-03T06:03:55.882+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/snowflake/c759145c-2f25-443e-b535-515b93cb77dc","signal_url":"https://onlylabs.fyi/signals/65c8e939-7b1e-4b75-9eeb-ee8d64323650","signal_json_url":"https://onlylabs.fyi/signals/65c8e939-7b1e-4b75-9eeb-ee8d64323650/signal.json","text":"job_opened · Statutory Accountant / Associate Accountant · signal_desk=hiring · occurred_at=2026-06-03T06:03:55.882+00:00 · url=https://jobs.ashbyhq.com/snowflake/c759145c-2f25-443e-b535-515b93cb77dc · raw={\"location\":\"IN-Pune\",\"team\":\"Finance\",\"ats\":\"ashby\"}"},{"ref":"E50","kind":"event","title":"Connect AI to Your Data: Simplify the Entire Development Lifecycle","date":"2026-06-02T23:56:00+00:00","date_source":"rss.item_date","source_url":"https://www.snowflake.com/content/snowflake-site/global/en/blog/data-development-simple-as-prompt","signal_url":"https://onlylabs.fyi/signals/6c92f79f-434e-4507-848e-3ad7e0030c1f","signal_json_url":"https://onlylabs.fyi/signals/6c92f79f-434e-4507-848e-3ad7e0030c1f/signal.json","text":"post_published · Connect AI to Your Data: Simplify the Entire Development Lifecycle · signal_desk=talking · occurred_at=2026-06-02T23:56:00+00:00 · url=https://www.snowflake.com/content/snowflake-site/global/en/blog/data-development-simple-as-prompt · raw={\"excerpt\":\"Discover how Snowflake Summit 2026 redefines data development for AI. Learn about Datastream, zero-copy integrations and self-managing pipelines.\"}"},{"ref":"E51","kind":"event","title":"Staff Escalation Manager","date":"2026-06-02T23:33:18.192+00:00","date_source":"ashby.publishedAt","source_url":"https://jobs.ashbyhq.com/snowflake/2644e559-5dbd-4246-bff5-f21c92ae07d7","signal_url":"https://onlylabs.fyi/signals/0ab05bb5-5a73-454d-821b-04144410e124","signal_json_url":"https://onlylabs.fyi/signals/0ab05bb5-5a73-454d-821b-04144410e124/signal.json","text":"job_opened · Staff Escalation Manager · signal_desk=hiring · occurred_at=2026-06-02T23:33:18.192+00:00 · url=https://jobs.ashbyhq.com/snowflake/2644e559-5dbd-4246-bff5-f21c92ae07d7 · raw={\"location\":\"CA-Ontario-Toronto\",\"team\":\"Global Support\",\"ats\":\"ashby\"}"},{"ref":"E52","kind":"event","title":"The Interoperable Lakehouse: Agency Over Your Data","date":"2026-06-02T22:19:00+00:00","date_source":"rss.item_date","source_url":"https://www.snowflake.com/content/snowflake-site/global/en/blog/interoperable-lakehouse-architecture","signal_url":"https://onlylabs.fyi/signals/f25718d5-9563-4494-8307-36fa454bf0ec","signal_json_url":"https://onlylabs.fyi/signals/f25718d5-9563-4494-8307-36fa454bf0ec/signal.json","text":"post_published · The Interoperable Lakehouse: Agency Over Your Data · signal_desk=talking · occurred_at=2026-06-02T22:19:00+00:00 · url=https://www.snowflake.com/content/snowflake-site/global/en/blog/interoperable-lakehouse-architecture · raw={\"excerpt\":\"Achieve true agency over your data with the Snowflake Interoperable Lakehouse. 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