Senior Software Engineer, Strategy Research Analytics

Voleon is a technology company that applies state-of-the-art AI and machine learning techniques to real-world problems in finance. For nearly two decades, we have led our industry and worked at the frontier of applying AI/ML to investment management. We have become a multibillion-dollar asset manager, and we have ambitious goals for the future. As a Senior Software Engineer in Strategy Research Analytics, you will lead the design, evolution, and long-term architecture of Voleon’s analytics infrastructure supporting research reporting and analysis across strategies. You will own critical recurring analytics pipelines and foundational datasets, while guiding the transition from fragmented, bespoke workflows toward a standardized, observable, and query-native analytics platform. In addition to hands-on implementation, you will shape technical direction, establish reliability standards, and drive consolidation efforts that improve consistency, scalability, and reproducibility across research analytics systems. You’ll collaborate closely with Data Scientists, Researchers, and Data Infrastructure teams to ensure analytics systems run reliably and produce consistent, queryable datasets. This role offers strong technical ownership within a mission-critical area of the research organization, with meaningful impact on research velocity and insight generation. Your Team The Research Analytics team sits within Research Engineering and works closely with Data Scientists and Researchers across all of Voleon's core strategies. We look for brilliant people with a passion for solving problems through innovation and engineering fundamentals. You’ll work in a collaborative environment that encourages creative thinking and efficient implementation. You’ll work alongside experienced engineers recruited from leading technology companies and selected from the sharpest minds at university programs. The team’s mission is to: - Stabilize existing analytics pipelines to ensure critical data is available for our data scientist and research partners - Implement monitoring/alerting and operational runbooks; participate in incident response and postmortems - Standardize outputs from strategy workflows into a unified analytics schema (tables, metrics definitions, partitioning strategy) - Improve dataset discoverability via documentation, schema contracts, and metadata/lineage primitives - Optimize query performance and cost for distributed engines (Presto/Spark) and columnar formats (Parquet/ORC) Responsibilities - Own implementation and on-going operation of recurring analytics pipelines (e.g., Airflow DAGs) including monitoring, alerting, and reliability improvements - Lead architectural evolution of the analytics platform, including schema standardization, DAG consolidation, and modernization of legacy workflows - Drive cross-team technical alignment when consolidating duplicated or inconsistent analytics outputs - Build and maintain base analytics tables and metrics with strong schema discipline and reproducible computation - Define and implement reliability standards (SLOs, observability patterns, runbooks) adopted across analytics pipelines - Improve transparency and usability through documentation, discoverability, and clear data contracts - Optimize distributed compute and SQL query performance; design data layouts (partitioning, file sizing) for columnar storage - Mentor engineers through design reviews and raise the bar for operational and modeling rigor

Requirements

• Bachelor’s degree in Computer Science or equivalent professional experience - 6+ years of experience building and operating analytics or data infrastructure systems - Strong proficiency in Python and SQL - Deep experience with distributed query engines and large-scale compute systems - Demonstrated ownership of large-scale or mission-critical data infrastructure - Strong data modeling expertise, including schema design, partitioning strategy, and reproducibility considerations - Expertise in metadata management, data lineage, and applying robust data governance principles Preferred Qualifications - Experience leading architectural migrations or major refactors of data platforms - Familiarity with AWS cloud technologies and on-prem compute clusters (e.g., Slurm, SSH, Unix) - Exposure to quantitative research or machine learning environments. “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 https://voleon.com/referrals/ to submit your referral.

• Highlight concrete examples of production‑grade pipelines you built, especially with Airflow, Presto, or Spark.
• Include a short portfolio or GitHub repo that shows data‑modeling decisions, schema contracts, and observability implementations.
• Prepare to discuss how you defined SLOs or runbooks in past roles; interviewers will probe your reliability mindset.
• Emphasize any experience translating bespoke research workflows into standardized, queryable datasets.
• During the interview, ask about the current state of their analytics schema and how the team measures pipeline reproducibility.
• When negotiating, reference the equity component typical for senior roles in hedge‑fund‑style tech firms.
• Watch for vague answers about ownership boundaries—clarify who owns post‑mortem actions and long‑term pipeline stewardship.