Senior Performance Engineer, Data Products
New
T
Turquoise HealthHealthcare Technology
This is a fully remote role within the United States., US business hoursFull-TimeSenior
Salary$172K - $195K
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Job Details
- Experience
- 5+ years
- Required Skills
- PythonSQLClickhouse
Requirements
- 5+ years of experience building and optimizing production data systems, data-intensive applications, or analytical products.
- Advanced SQL skills.
- Strong proficiency with Python or another general-purpose programming language.
- Experience optimizing analytical workloads in column-oriented databases or data warehouses.
- Experience changing how data is organized or stored to improve application performance, such as restructuring tables or precomputing results.
- Experience using query plans, profiling, benchmarks, and production telemetry to diagnose performance problems.
- Ability to reason across system boundaries, including databases, APIs, backend services, and client request patterns.
- Demonstrated hypothesis-driven approach to performance work.
- Product judgment and ability to identify when changing the product experience is more effective than changing the underlying data system.
Responsibilities
- Own performance investigations across the product and data stack, moving from slow queries or poor user experiences to measurable root causes and production improvements.
- Design and evaluate data architecture changes.
- Partner with Product, frontend, and backend engineers to adapt product behavior through techniques such as request batching, debouncing, caching, and pagination.
- Develop representative benchmarks for important combinations of filters, datasets, and product workflows.
- Measure query latency, throughput, concurrency, and infrastructure consumption, with particular attention to datastore performance.
- Evaluate the likely performance impact of upstream dataset changes before they reach production.
- Assess future product requests and recommend technical or product approaches that balance functionality, responsiveness, scalability, and cost.
- Prototype competing solutions and validate the selected approach in production.
- Improve performance instrumentation, monitoring, and regression detection across data-powered product experiences.
- Establish reusable patterns and capabilities for the Data Product platform.
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