Job Description Summary
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, which translates to 2-3 days minimum per week in the office.
In this role, you’ll be a key contributor to HarbourVest’s data transformation journey - bridging the gap between raw data ingestion and analytics-ready assets that power AI, data science and business intelligence (BI) tools. You’ll work across squads and domains to design modular, analytics-focused data workflows, model business logic, and deliver actionable insights. This hybrid role combines engineering rigor with analytical depth and business fluency, making it ideal for professionals who thrive at the intersection of data, technology, and strategy.
The ideal candidate is someone who:
- Has showcased proficiency in analytics-focused positions encompassing BI, data modeling, and data pipeline development.
- Demonstrates strong SQL/Python and data modeling skills, with experience building modular, analytics-focused data workflows that support downstream consumption, reuse and alignment with business logic.
- Has hands-on experience with BI tools (e.g., Power BI) and understands how to translate business needs into data models and visualizations.
- Is comfortable working in cloud-based environments (e.g., Azure Synapse, Snowflake).
- Understands the principles of analytics development and applies software engineering best practices (modularity, testing, version control) to data workflows.
- Has a good sense of business context and can convert data findings into actionable insights.
- Is passionate about enabling AI and data science by delivering clean, validated, and reusable datasets.
- Works closely in multi-functional teams and communicates effectively with both technical and business partners.
What you will do:
- Design and maintain analytics-ready datasets that support AI/ML experimentation, predictive modeling, and BI visualizations.
- Build adaptable data pipelines using SQL/Python and cloud tools, explore AI tools for quicker analytics and fresh operational approaches.
- Collaborate with Data Scientists, BI Analysts, and Data Engineers to translate business logic into reusable transformations that bridge tactical prototyping with strategic data architecture. These outputs are designed for seamless transition into production-grade, enterprise-level data assets that power governed, scalable analytics products.
- Implement data validation, testing, and observability to ensure trust and reliability in data delivery.
- Contribute to the development of data products within the squad operating model.
- Participate in technical reviews and help define standard processes for analytics engineering.
What you bring:
- Deep understanding of data modeling techniques (e.g., star/snowflake schemas).
- Strong SQL/Python proficiency and experience with cloud data platforms, with exposure to emerging analytics engineering tools such as dbt, Dagster or similar modern data transformation frameworks.
- Familiarity with data visualization tools and dashboard development.
- Experience with version control systems (e.g., Git) and CI/CD workflows.
- Solid grasp of REST APIs
- Effective communication abilities with a business focus and the ability to collaborate across diverse teams..
- A commitment to continuous learning and professional development.
- Passion for high-quality work, excellence in documentation, and attention to detail.
Education Preferred:
- Bachelor of Science (B.S) or equivalent experience in a STEM field preferred.
- Possessing a Master’s degree is beneficial but not mandatory; relevant experience is also taken into account.
Experience:
- Proven ability in analytics engineering, BI development, or data pipeline design.
- Prior experience in financial services or investment data is a plus.
#LI-Hybrid