Engineering Manager/Tech Lead Manager - Runtime Engineering

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.

Location: Bellevue, WA or Menlo Park, CA

The Spark Runtime Engineering Team brings Snowflake’s performance, efficiency, and reliability to Spark workloads. Our mission is to help customers scale data processing while preserving the familiar Spark APIs and semantics they rely on. We build the compiler and runtime integration that translate Spark workloads into efficient execution on Snowflake’s cloud-native query engine. This includes tackling complex challenges in compiler infrastructure, query-plan lowering, Spark compatibility, execution integration, and end-to-end observability to deliver faster, more cost-efficient data processing at scale.

We are seeking a hands-on Engineering Manager who combines deep technical expertise with strong people leadership. You will shape the team’s technical direction, stay actively engaged in architecture and critical engineering decisions, and hire, mentor, and grow a high-performing engineering team to advance the future of Spark on Snowflake.

You will lead a team of approximately 5–8 engineers building production compiler and runtime capabilities for Spark workloads on Snowflake. You will guide architectural decisions, review critical designs and code, and help investigate complex correctness, performance, and reliability issues. In partnership with compiler, query engine, runtime, and product teams, you will turn customer needs into a focused roadmap and measurable engineering outcomes.

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  Set technical direction for Spark query compilation and runtime integration, including intermediate representations, type resolution, semantic compatibility, query-plan transformations, and efficient SQL generation.

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  Improve end-to-end workload performance and cost-efficiency through profiling, compilation-latency reduction, metadata-access improvements, and collaboration with query-engine teams.

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  Establish strong correctness and compatibility practices, including differential testing against Apache Spark, regression coverage, and clear handling of unsupported behavior.

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  Own the production quality of the team’s components, including observability, incident response, fault recovery, and safe rollout practices.

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  Hire, coach, and develop engineers; provide actionable feedback, manage performance, and support career growth across experience levels.

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  Partner with senior technical leaders and Product Management to prioritize investments, manage dependencies, and deliver a realistic roadmap.

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  Validate changes against representative customer workloads and track measurable improvements in compatibility, latency, reliability, and cost-efficiency.

• 2+ years of direct engineering management experience, with demonstrated success developing engineers and delivering production systems.

• 5+ years of hands-on software engineering experience, including substantial work on compilers, query-processing systems, or runtime infrastructure.

• Deep expertise in at least one relevant area, such as 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, together with experience debugging complex production issues.

• Ability to translate technical and customer requirements into engineering priorities and collaborate effectively across teams and locations.

• 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.