Senior Engineer, Competitive Benchmarking & Performance

b Overview /b p In this role you will lead competitive benchmarking within the Elasticsearch Performance team, building representative workloads and automated experiments to compare Elasticsearch with alternatives. You will publish findings that guide engineering improvements and help customers make informed decisions. You’ll connect deep performance analysis to product enhancements and public technical work. This is a hands-on role focused on benchmarks, with collaboration across engineering and content partners. /p b Pay / Benefits /b ul li Competitive pay /li li Health coverage for you and your family /li li Flexible locations and schedules /li li Generous vacation days /li li Donation matching up to 2000 USD /li li Parental leave (minimum 16 weeks) /li /ul b Responsibilities /b ul li Own prioritized competitive benchmarking initiatives across search, vector and hybrid retrieval, and analytical and observability workloads /li li Build and maintain automated benchmark harnesses, datasets, query sets, and load profiles reflecting real-world usage /li li Design fair comparisons with documented versions, configurations, budgets, and functional differences /li li Evaluate latency, throughput, resource consumption, and cost efficiency; measure recall or relevance for retrieval workloads /li li Adapt and extend workload-appropriate tools (e.g., Elasticsearch Rally, ClickBench, vector benchmark frameworks) /li li Investigate performance differences with profiling and experiments; collaborate with engineering to identify causes and validate improvements /li li Evaluate self-managed and managed cloud deployments, including serverless offerings /li li Publish reproducible benchmark artifacts and technical reports; translate findings into product improvements /li /ul b Key requirements /b ul li Solid software engineering skills; experience building automation and analysis tools in Python or similar /li li Hands-on experience diagnosing performance in distributed systems, databases, or search infrastructure /li li Deep knowledge in at least one area (search, vector retrieval, analytical databases, or observability) and ability to learn new systems /li li Experience designing representative datasets and workloads with query patterns, concurrency, and ingestion behavior /li li Strong experimental judgment and understanding of warm-up, cache effects, and measurement noise /li li Experience building reproducible test environments and documenting configurations and results /li li Clear technical writing and communication skills; ability to explain complex findings and limitations /li li Strong collaboration skills and ability to turn performance data into engineering recommendations /li /ul ul li Clear technical writing /li li Collaborative mindset /li li Problem solving under measurement noise /li li Python or another relevant programming language /li li Performance evaluation of distributed systems, databases, or search infrastructure /li li Benchmarking tools (Elasticsearch Rally, ClickBench, vector search benchmarks) /li /ul