Principal Software Engineer - Semantic Views

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.

We are the Snowflake Semantic Views team. We build the semantic layer at the core of the Snowflake platform — the system that lets customers define logical data models (tables, dimensions, metrics, and the relationships between them) once, and then query them consistently across BI tools, SQL, and AI agents. Semantic Views turn raw physical schemas into governed, reusable business definitions, so that "revenue," "active customer," or "gross margin" mean the same thing no matter who or what is asking. We own the semantic model itself, compiler elements and query rewriter that expand semantic queries into optimized SQL over petabyte-scale data, and the surfaces that expose this layer to Cortex Analyst and the broader agent ecosystem. Our work sits at the intersection of query language, metadata, and AI-driven analytics, and it is foundational to how Snowflake customers get trustworthy answers from their data.

AS A PRINCIPAL SOFTWARE ENGINEER AT SNOWFLAKE YOU WILL:

• Solve real business needs at large scale by applying your software engineering and analytical problem-solving skills.

• Design, develop, and support the semantic modeling layer that translates semantic queries into correct, high-performance SQL.

• Create architecture and design, influence the semantic layer roadmap, and take ownership over new capabilities spanning metrics, dimensions, relationships, and governance.

• Ensure semantic queries produce correct results while meeting the performance and scale demands of interactive analytics over petabyte-scale data.