Staff Software Engineer — Dynamic Tables, Performance

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 confidentiality and security standards for handling sensitive data. Snowflake employees must abide by the company’s data security plan as an essential part of their duties. It is every emplo