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Snowflake

Engineering Manager – Tech Lead Manager, Runtime Engineering

Snowflake

. Lead a team of approximately 5–8 engineers building production compiler and runtime capabilities for Spark workloads on Snowflake .

Posted 10/22/2026full-timeUnited StatesSenior💰 $236,000 - $339,200 per yearWebsite

Core Competencies

Role fit
Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in compiler architecture, query processing, and runtime integration while leading engineering teams to deliver high-quality production systems. Proficient in Scala, Java, and C++ with a strong focus on performance analysis and system reliability.

Highest-signal resume keywords
Engineering Management ExperienceCompiler Architecture ExpertiseProficiency In Scala, Java, C++Systems Fundamentals KnowledgeApache Spark Internals Experience

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills
Compiler ArchitectureIR TransformationsRelational Query ProcessingLanguage SemanticsType SystemsPerformance AnalysisDebugging Complex IssuesSQL GenerationQuery OptimizationDifferential Testing
Soft Skills
Team LeadershipCollaborationPerformance ManagementTechnical DirectionFeedback Provision
Tools & Technologies
Apache SparkCatalystSpark ConnectDataFrame APIsMetadata CachingProduction Observability
Industry Keywords
Production SystemsWorkload PerformanceCost-EfficiencyCompatibility TestingIncident Response

Tech Stack

Tools & technologies
ApacheJavaScalaSparkSQLC++

About the role

Key responsibilities & impact
  • Lead a team of 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
  • Guide architectural decisions, review critical designs and code, and investigate complex correctness, performance, and reliability issues
  • Improve end-to-end 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, and handling of unsupported behavior
  • Own production quality, including observability, incident response, fault recovery, and safe rollout practices
  • Hire, coach, and develop engineers; provide feedback, manage performance, and support career growth
  • Partner with senior technical leaders and Product Management to prioritize investments, manage dependencies, and deliver a roadmap
  • Validate changes against representative customer workloads and track improvements in compatibility, latency, reliability, and cost-efficiency

Requirements

What you’ll need
  • 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
  • Preferred: experience with Apache Spark internals, Catalyst, Spark Connect, or DataFrame APIs
  • Preferred: experience with relational algebra, SQL generation, query optimization, or database execution systems
  • Preferred: experience with Scala/JVM performance, native runtime functions, metadata caching, or compilation-performance optimization
  • Preferred: experience building compatibility-test frameworks, differential testing, performance benchmarks, or production observability for data systems
  • Preferred: experience leading systems through customer adoption, production hardening, and staged releases