Remote Analytics Platform Engineer: Research Strategy

CV in; Northern (KY); Hybrid; 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

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