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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 to submit your referral. For more details regarding eligibility, terms and conditions please make sure to review the Voleon Referral Bonus Program . 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.

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 to submit your referral. For more details regarding eligibility, terms and conditions please make sure to review the Voleon Referral Bonus Program . 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.

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 to submit your referral. For more details regarding eligibility, terms and conditions please make sure to review the Voleon Referral Bonus Program . 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.

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. 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. Responsibilities 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 Requirements 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 . 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.

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. 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. Responsibilities 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 Requirements 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 . 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.

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. 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. Responsibilities 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 Requirements 8+ 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 . 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.

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. 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. Responsibilities 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 Requirements 8+ 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 . 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.

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 to submit your referral. For more details regarding eligibility, terms and conditions please make sure to review the Voleon Referral Bonus Program . 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.