Staff Software Engineer - Dynamic Tables, Performance

- 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 across a DAG of dependencies, and automatically incrementalizing 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.

- 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).

- 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.

- Experience with a major analytical DBMS (BigQuery, Redshift, Databricks, Teradata, Oracle, SQL Server).

- 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.