Engineering Manager (Spark)

Location: Hybrid in Bellevue, WA or Menlo Park, CA, United States

Salary: $236K–$339.2K

Company

Snowflake provides a cloud-native query engine and data-processing platform for enterprise workloads.

What you will do

• Lead approximately 5–8 engineers building production compiler and runtime capabilities for Spark workloads on Snowflake.
• Set technical direction for Spark query compilation and runtime integration, including intermediate representations, type resolution, semantic compatibility, query-plan transformations, and SQL generation.
• Improve workload performance and cost-efficiency through profiling, compilation-latency reduction, metadata-access improvements, and collaboration with query-engine teams.
• Establish correctness and compatibility practices, including differential testing against Apache Spark, regression coverage, observability, incident response, fault recovery, and safe rollouts.
• Partner with compiler, query engine, runtime, product, and senior technical teams to prioritize investments, manage dependencies, and deliver a measurable roadmap.
• Hire, coach, and develop engineers while validating changes against customer workloads and tracking compatibility, latency, reliability, and cost-efficiency improvements.

Requirements

• 2+ years of direct engineering management experience, including developing engineers and delivering production systems.
• 5+ years of hands-on software engineering experience with substantial work on compilers, query-processing systems, or runtime infrastructure.
• Deep expertise in at least one relevant area: compiler architecture and IR transformations, relational query processing, language semantics and type systems, or runtime integration.
• Proficiency in Scala, Java, C++, or a comparable implementation language, with the ability to review production code and guide technical decisions.
• Strong systems fundamentals, including concurrency, memory behavior, and performance analysis, plus experience debugging complex production issues.
• Ability to translate technical and customer requirements into engineering priorities and collaborate effectively across teams and locations.

Nice to have

• Experience with Apache Spark internals, Catalyst, Spark Connect, or DataFrame APIs.
• Experience with relational algebra, SQL generation, query optimization, or database execution systems.
• Experience with Scala/JVM performance, native runtime functions, metadata caching, or compilation-performance optimization.
• Experience building compatibility-test frameworks, differential testing, performance benchmarks, or production observability for data systems.
• Experience leading systems through customer adoption, production hardening, and staged releases.
• A degree in Computer Science, Computer Engineering, or a related field, or equivalent practical experience.

Culture & Benefits

• AI-native, experimental approach to solving problems and accelerating impact.
• Low-ego environment focused on challenging ordinary thinking and innovation.
• Opportunity to work on technically challenging compiler and runtime problems with measurable effects on data-processing correctness, reliability, and cost-efficiency.