Principal Software Engineer II - Interoperable Foundations

About this role

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's rise as a revolutionary cloud data warehouse was fueled by a key architectural tenet: separating compute from storage through a world-class metadata engine. Now, as we transition to the “Interoperable Data + AI Cloud”, metadata and open standards are the keys to unlocking the agentic enterprise at scale.

AI agents cannot reason over raw data; they require deep context, strict governance, and instant interoperability. To usher in this new era, we are seeking AI-native systems thinkers to architect the foundational substrate that allows autonomous AI to query, understand, and act upon exabyte-scale datasets regardless of whether that data is stored directly within Snowflake or resides externally in a customer managed lakehouse.

The Mission: Interoperability and Metadata as the Engine for AI

As part of the Snowflake Interoperable Foundations organization, you will lead a 0->1 ground-up redesign of our core metadata and storage architecture.

Metadata engineering and open standards (like Apache Iceberg) are the linchpins of Snowflake's future. They are what allow us to bring Snowflake's industry-leading performance and governance to data sitting anywhere in the world. Your mission is to evolve this architecture so that open, multi-format data lakes are not just searchable by humans, but inherently AI-ready by transforming interoperable metadata into the contextual engine that powers agentic innovation.