Job Opportunities API

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. About Dynamic Tables - Dynamic Tables (DTs) are Snowflake's declarative streaming transformation primitive. Customers define a SQL query and a freshness target; Snowflake handles the rest: orchestrating refreshes, maintaining snapshot consistency https://dl.acm.org/doi/10.1145/3722212.3724455 across a DAG of dependencies, and automatically incrementalizing https://dl.acm.org/doi/10.1145/3589776 the computation so that cost scales with what changed. Dynamic Tables is one of the fastest growing products at Snowflake and is a core part of Snowflake’s Data Engineering strategy. The Dynamic Tables performance team is responsible for making incremental refresh fast, predictable, and cost-efficient across increasingly complex query shapes. As a Staff Engineer on this team, you will own the technical direction for critical performance initiatives and be a force multiplier for the engineers around you. WHAT YOU'LL DO - Lead the design and implementation of performance improvements to the incremental view maintenance engine, including multi-join incrementalization, novel incrementalization semantics, incremental window functions, and stacked operations. - Help define the roadmap for the incremental view maintenance engine, identifying key performance, scalability, and correctness milestones, prioritizing high-impact enhancements, and aligning technical investments with product and research goals. - Collaborate across teams to co-design improvements that benefit incremental pipelines. - Mentor engineers, drive design reviews, and raise the technical bar for the team through architectural leadership and high-quality code. - Contribute to the research and publication roadmap; the team has an active presence at top-tier database conferences (SIGMOD, VLDB). WHAT WE'RE LOOKING FOR - 10 + years of experience building and optimizing large-scale data systems, with deep expertise in at least one of: query optimization, incremental/stream processing, or materialized view maintenance. - Strong computer science fundamentals — algorithms, data structures, and distributed systems design. - Proficiency in C++ or Java; experience with systems-level performance analysis (profiling, benchmarking, regression detection). - Demonstrated ability to lead multi-engineer, cross-team technical initiatives and translate ambiguous problem spaces into concrete engineering plans. - Experience operating systems at cloud scale (multi-tenant SaaS, petabyte-scale data, thousands of concurrent workloads). - Strong written and verbal communication skills; ability to present complex technical trade-offs to both engineering and product audiences. NICE TO HAVE - Experience with a major analytical DBMS (BigQuery, Redshift, Databricks, Teradata, Oracle, SQL Server). - Familiarity with stream processing algorithms. - Experience with CDC pipelines, data lake architectures (Iceberg, Delta), or the broader data engineering ecosystem (dbt, Airflow, Fivetran). - Advanced degree (MS or PhD) in Computer Science, with emphasis on database systems. Every Snowflake employee is expected to follow the company’s confide