Senior Director, Analytics Engineering, Data Analytics & AI

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At 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.

Snowflake is about empowering enterprises to achieve their full potential with Data & AI. With a culture that's all in on impact, innovation, and collaboration, Snowflake is the sweet spot for building big, moving fast, and taking technology and careers to the next level.

We're hiring for a Senior Director of Analytics Engineering to join our Data, Analytics, and AI organization (DAA), reporting directly to the Chief Data & Analytics Officer. You will lead a team of 20+ Analytics Engineers that build and operate the pipelines behind Snowflake's revenue, bookings, people analytics, and go-to-market reporting within our governed data platform.

This role has an in-office requirement — you must be able to work out of our Menlo Park office a minimum of 3 days a week.

IN THIS ROLE YOU WILL

• Lead and grow the Analytics Engineering organization: Manage through your direct reports (team leads/managers), setting priorities and technical strategy across the team.

• Drive Snowflake’s internal data transformation: Lead your teams' ongoing work to create an AI ready data foundation, with an emphasis on documentation, contracts, context, and governance.

• Own Snowflake’s internal analytics agents: Build, improve, and maintain general purpose analytics agents - and underlying context and semantic layers - that can be used across the organization including evals, orchestration instructions, and ground truth data sets