Leads design and implementation of performance improvements for Snowflake's Dynamic Tables incremental view maintenance engine, focusing on complex query shapes, scalability, and cost-efficiency. Requires 10+ years in large-scale data systems with expertise in query optimization or stream processing, plus C++/Java proficiency.
236k – 339k/yr
Hybrid10+ YOEBackend Engineering
About the role
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.
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