Strategy Platform owns the infrastructure between quantitative research and live trading. Our systems orchestrate the transformation of external market data into features consumed by ML-driven trading strategies, manage strategy deployments from research handoff through production, and provide the tooling that lets researchers iterate quickly without compromising production reliability.

This is a central platform team. We are consolidating and unifying systems that grew organically to serve different parts of the trading lifecycle, building and migrating to shared abstractions across data ingestion, feature computation, deployment and production trading operations. The team balances deep knowledge of how our trading strategies operate with building foundational layers that support the company's continued rapid growth.

The Role

You will own significant pieces of our platform end to end: designing, building, operating, and evolving them. Concretely:

• 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

You might be a good fit if you have

• 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

"Friends of Voleon" Candidate Referral Program

If you have a great candidate in mind for this role and would like to have the potential to earn $15,000 if your referred candidate is successfully hired and employed by The Voleon Group, please use this form to submit your referral. For more details regarding eligibility, terms and conditions please make sure to review the Voleon Referral Bonus Program.