Quantitative Developer, Data Optimization
About the position For over forty years, HarbourVest has been home to a committed team of professionals with an entrepreneurial spirit and a desire to deliver impactful solutions to our clients and investing partners. As our global firm grows, we continue to add individuals who seek a collaborative, open-door culture that values diversity and innovative thinking. In our collegial environment that’s marked by low turnover and high energy, you’ll be inspired to grow and thrive. Here, you will be encouraged to build on your strengths and acquire new skills and experiences. We are committed to fostering an environment of inclusion that promotes mutual respect among all employees. Understanding and valuing these differences optimizes the potential of both the individual and the firm. HarbourVest is an equal opportunity employer. This position will be a hybrid work arrangement. You will receive 18 remote workdays per quarter to use at your discretion, subject to manager approval. For example, you may choose to work in the office 4 days per week and take one remote day weekly (typically 13 weeks per quarter), leaving 5 additional remote days to be used as needed. Seated within our Quantitative Investment Science group, this position focuses on transforming raw warehouse data into application-ready, performant datasets optimized for QIS's computational models and analysis pipelines. This engineer ensures that every downstream computation starts with clean, fast, trustworthy data - reducing manual rework and enabling the team to move quickly with confidence. This role is ideal for engineers who love making data pipelines bulletproof and performant. The ideal candidate is someone who has: A passion for data quality, reliability, and performance Strong attention to detail and a systematic approach to problem-solving Strong collaboration abilities - you'll partner closely with the Performance Tech Lead and application developers A love for building systems that "just work" Interest in finance and quantitative applications
Responsibilities
- Optimize middle-tier OLTP schemas and materialized tables for efficient application data access
- Refactor heavy bottom-up models and preprocessing pipelines, narrowing inputs to essential subsets
- Implement Dagster/dbt data agreements, automated tests, and drift detection for reliability
- Build and maintain data validation layers that catch issues before they reach production
- Collaborate with the Performance Tech Lead to guarantee smooth data transfer between storage and computation
- Build monitoring and alerting for data pipeline health and latency
- Document data lineage and transformation logic for team transparency
Requirements
- Strong proficiency in Python and its data stack (pandas, numpy, polars)
- Expert-level SQL skills including query optimization, indexing strategies, and execution plan analysis
- Experience with modern data orchestration tools (Dagster, dbt, or Airflow)
- Understanding of OLTP vs OLAP patterns and when to use each
- Experience implementing data contracts, schema validation, and automated testing for data pipelines
- Experience with data quality frameworks and identifying drift
- Understanding of materialized views, incremental processing, and caching strategies
- Experience with cloud data platforms (Azure preferred)
- Awareness of performance optimization at the data layer
Nice-to-haves
- Experience with financial data, time series data, or quantitative analysis pipelines
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