Staff Software Engineer, Strategy Platform
Staff engineer owns end-to-end platform systems for data access, validation, orchestration, and ML strategy deployment in a quantitative hedge fund. Requires 5+ years in backend/data pipelines with strong debugging skills and CS degree preferred.
About the job
Responsibilities
- Own how data is accessed, validated, orchestrated, and catalogued across research and production
- Engineer smooth deployment processes for research experiments into production
- Develop tooling to integrate data from diverse vendors, unifying symbol mappings for data consistency
- Support data pipelines with strong temporal semantics under a range of latency and correctness requirements
- Sequence platform migrations that move the firm toward shared abstractions while minimizing disruption to active trading systems
- Lead complex projects spanning the company, collaborating across research, legal, trading, finance operations, data, and infrastructure teams
- Build tooling to support integration with new assets and markets
- Improve observability across the strategy lifecycle, including data cataloguing, experiment tracking, and production SLAs
Requirements
- 5+ years of experience in backend, data pipelines, or platform engineering
- Owned platform systems that other teams depend on daily, made real decomposition decisions (data access layers, API versioning, data models, migration sequencing), and improved those systems while they were actively in use
- Strong debugging and observability instincts. You orient quickly in unfamiliar codebases and datasets, particularly across data pipelines with many upstream sources and downstream consumers
- Computer Science Degree, or equivalent experience
Preferred Qualifications
- Experience with Airflow, Dagster, Spark, Iceberg, Trino, Flink, or similar data infrastructure
- Familiarity with ML infrastructure patterns (feature stores, model serving, experiment tracking)
- Python Fluency
Skills
Python, Airflow, Dagster, Spark, Iceberg, Trino, Flink, Data Pipelines, Observability, ML Infrastructure
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