Software Engineer, Strategy Research Analytics

CV in; Northern (KY); On-site; USD 120,000 - 150,000; Full time

Job summary

Voleon is seeking a Software Engineer, Strategy Research Analytics to own recurring analytics pipelines and steer the evolution of the analytics platform. You will standardize schemas, consolidate DAGs, and improve reliability for research reporting and analysis across strategies.

Collaborate with data scientists and researchers to ensure reliable, queryable datasets. You will help design data layouts and optimize distributed compute, contributing to reproducible analytics that support velocity

Qualifications

• Bachelor’s degree in Computer Science or equivalent.
• 3+ years of experience building and operating analytics or data infrastructure systems in production environments.
• Strong proficiency in Python and SQL across development, debugging, and optimization scenarios.
• Deep experience with distributed query engines and large-scale compute systems that handle complex analytical workloads.
• Demonstrated ownership of large-scale or mission-critical data infrastructure with a track record of reliability and uptime.
• Strong data modeling expertise, including schema design, partitioning strategy, and reproducibility considerations for analytical datasets.
• Expertise in metadata management, data lineage, and applying robust data governance principles to ensure compliance and usability.

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 to reduce fragmentation.
• Build and maintain base analytics tables and metrics with strong schema discipline and reproducible computation for research consumption.
• Define and implement reliability standards (SLOs, observability patterns, runbooks) adopted across analytics pipelines to improve operational rigor.
• Improve transparency and usability through documentation, discoverability, and clear data contracts that connect producers and consumers of analytics.
• Optimize distributed compute and SQL query performance; design data layouts (partitioning, file sizing) for columnar storage formats like Parquet and ORC.
• Mentor engineers through design reviews and raise the bar for operational and modeling rigor across the analytics platform.
• Stabilize existing analytics pipelines to ensure critical data is available for our data scientist and research partners on an ongoing basis.
• Implement monitoring/alerting and operational runbooks; participate in incident response and postmortems to drive reliability improvements.
• Standardize outputs from strategy workflows into a unified analytics schema (tables, metrics definitions, partitioning strategy) for consistent consumption.
• Improve dataset discoverability via documentation, schema contracts, and metadata/lineage primitives that support auditability and reuse.
• Optimize query performance and cost for distributed engines (Presto/Spark) and columnar formats (Parquet/ORC) to support efficient research analytics.
• Collaborate closely with Data Scientists, Researchers, and Data Infrastructure teams to ensure analytics systems run reliably and produce consistent, queryable datasets.

Skills

Python

SQL

Data modeling

Education

Bachelor’s degree in Computer Science

Tools

Airflow

Presto

Spark

Parquet

ORC

Job description

Software Engineer, Strategy Research Analytics at voleon.

About the role 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 this role, you will shape technical direction, establish reliability standards, and drive consolidation efforts that improve consistency, scalability, and reproducibility across research analytics systems. You will collaborate closely with Data Scientists, Researchers, and Data Infrastructure teams to ensure analytics systems run reliably and produce consistent, queryable datasets. This position offers strong technical ownership within a mission-critical area of the research organization, with meaningful impact on research velocity and insight generation. You will work alongside internationally recognized experts in artificial intelligence and machine learning research as well as highly experienced finance and technology professionals.

Key facts Location: Remote, United States Engagement: full_time

What you'll do

• 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 to reduce fragmentation.
• Build and maintain base analytics tables and metrics with strong schema discipline and reproducible computation for research consumption.
• Define and implement reliability standards (SLOs, observability patterns, runbooks) adopted across analytics pipelines to improve operational rigor.
• Improve transparency and usability through documentation, discoverability, and clear data contracts that connect producers and consumers of analytics.
• Optimize distributed compute and SQL query performance; design data layouts (partitioning, file sizing) for columnar storage formats like Parquet and ORC.
• Mentor engineers through design reviews and raise the bar for operational and modeling rigor across the analytics platform.
• Stabilize existing analytics pipelines to ensure critical data is available for our data scientist and research partners on an ongoing basis.
• Implement monitoring/alerting and operational runbooks; participate in incident response and postmortems to drive reliability improvements.
• Standardize outputs from strategy workflows into a unified analytics schema (tables, metrics definitions, partitioning strategy) for consistent consumption.
• Improve dataset discoverability via documentation, schema contracts, and metadata/lineage primitives that support auditability and reuse.
• Optimize query performance and cost for distributed engines (Presto/Spark) and columnar formats (Parquet/ORC) to support efficient research analytics.
• Collaborate closely with Data Scientists, Researchers, and Data Infrastructure teams to ensure analytics systems run reliably and produce consistent, queryable datasets.

Requirements

• Bachelor’s degree in Computer Science or equivalent professional experience as a foundational academic or practical credential.
• 3+ years of experience building and operating analytics or data infrastructure systems in production environments.
• Strong proficiency in Python and SQL across development, debugging, and optimization scenarios.
• Deep experience with distributed query engines and large-scale compute systems that handle complex analytical workloads.
• Demonstrated ownership of large-scale or mission-critical data infrastructure with a track record of reliability and uptime.
• Strong data modeling expertise, including schema design, partitioning strategy, and reproducibility considerations for analytical datasets.
• Expertise in metadata management, data lineage, and applying robust data governance principles to ensure compliance and usability.

Nice to have

• Experience leading architectural migrations or major refactors of data platforms that involve significant operational change.
• Familiarity with AWS cloud technologies and on-prem compute clusters (e.g., Slurm, SSH, Unix) used in high-performance analytical environments.
• Exposure to quantitative research or machine learning environments where analytics directly support insight generation and decision-making.

Practical notes

This role is full-time and remote within the United States. No specific compensation details are provided in the source material. The position involves ongoing operations, incident response, and participation in reliability practices such as postmortems. There are no published deadlines or visa sponsorship details included in the available source information.