Diagnose and fix

Own performance investigations across the product and data stack, taking a slow query or poor user experience to a measured root cause and a production fix. Example: a critical filter and data-source combination is now x% faster in production.

Track latency, throughput, concurrency, and infrastructure cost, with a focus on datastore efficiency (we run on ClickHouse Cloud). Example: a product workflow now uses z% fewer ClickHouse resources without compromising the user experience.

Build representative benchmarks for the filter, dataset, and workflow combinations that matter most.

Evaluate the performance impact of upstream dataset changes before they reach production. Example: an incoming change is expected to increase latency by y%, and we have a mitigation plan before launch.

Assess future product requests and recommend technical or product approaches that balance functionality, responsiveness, scalability, and cost.

Design and evaluate data architecture changes, such as restructuring tables, precomputing results, and changing indexes.

Partner with Product, frontend, and backend engineers to adapt product behavior to the data, using techniques like request batching, debouncing, caching, and pagination.

Prototype competing solutions, communicate their tradeoffs clearly, and validate the chosen approach in production.

Build for the long term

Improve performance instrumentation, monitoring, and regression detection across data-powered product experiences.

Establish reusable patterns as the Data Product platform becomes a supporting layer for more products and teams.

Senior Software Engineer, AI

Responsibilities

Qualification

Required

Preferred

Benefits

Company

Turquoise Health

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