Job description

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. Your colleagues will include internationally recognized experts in artificial intelligence and machine learning research as well as highly experienced finance and technology professionals. In addition to our enriching and collegial working environment, we offer highly competitive compensation and benefits packages, technology talks by our experts, a beautiful modern office, daily catered lunches, and more. As a 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 - 3+ 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 voleon.com to submit your referral. For more details regarding eligibility, terms and conditions please make sure to review the Voleon Referral Bonus Program voleon.com. EQUAL OPPORTUNITY EMPLOYER The Voleon Group is an Equal Opportunity employer. Applicants are considered without regard to race, color, religion, creed, national origin, age, sex, gender, marital status, sexual orientation and identity, genetic information, veteran status, citizenship, or any other factors prohibited by local, state, or federal law.

Requirements

Must-haves

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)
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
Bachelor’s degree in Computer Science or equivalent professional experience
3+ 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

Nice-to-haves

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.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimachine learningmlsqlpythonaws

Platform: ashby
Effort: quick