About the Role
We are looking for a Backend Engineer to design, build, and operate a metrics platform that supports statistical evaluation at scale. You will own core components of the system, including data models, storage layouts, compute pipelines, and developer-facing frameworks for writing and testing metrics.
This role involves close collaboration with metric authors and metric consumers across engineering, analytics, and QA, ensuring that metric results are reliable, performant, and easy to use end to end.
What You’ll Do
- Own and evolve the metrics platform, including schemas, storage layouts optimized for high-volume writes and fast analytical reads, and clear versioning strategies
- Build and maintain a framework for writing and running metrics, including interfaces, examples, local execution, and CI compatibility checks
- Design and implement testing systems for metrics and pipelines, including unit, contract, and regression tests using synthetic and sampled data
- Operate compute and storage systems in production, with responsibility for monitoring, debugging, stability, and cost awareness
- Partner with metric authors and stakeholders across development, analytics, and QA to plan changes and roll them out safely
What You’ll Need
Strong experience using Python in production , including asynchronous programming (e.g., asyncio, aiohttp, FastAPI)Advanced SQL skills, including complex joins, window functions, CTEs, and query optimization through execution plan analysisSolid understanding of data structures and algorithms , with the ability to make informed performance trade-offsExperience with databases , especially PostgreSQL (required); experience with ClickHouse is a strong plusUnderstanding of OLTP vs. OLAP trade-offs and how schema and storage decisions affect performanceExperience with workflow orchestration tools such as Airflow (used today), Prefect, Argo, or DagsterFamiliarity with data libraries and validation frameworks (NumPy, pandas, Pydantic, or equivalents)Experience building web services (FastAPI, Flask, Django, or similar)Comfort working with containers and orchestration tools like Docker and KubernetesExperience working with large-scale datasets and data-intensive systemsNice to Have
Ability to read and make small changes in C++ codeExperience building ML-adjacent metrics or evaluation infrastructureFamiliarity with Parquet and object storage layout / partitioning strategiesExperience with Kafka or task queuesExposure to basic observability practices (logging, metrics, tracing)