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 4 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 other applications. You’ll work across teams 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 engineering foundations and enjoys building reliable, production-grade analytics solutions.
Is comfortable working across data science, AI engineering, and DevOps responsibilities.
Understands the principles of analytics development and applies software engineering standard methodologies (modularity, testing, version control) to data workflows.
Demonstrates a problem-solving attitude with solid attention to detail, organisation, and documentation.
Thrives in a collaborative, inclusive, and professional environment.
Takes a practical, business-focused approach to analytics and AI delivery.
Is intellectually curious, cognitively flexible and motivated by continuous learning and evolving technologies, particularly in AI-enabled analytics.
What you will do:
Productionise Data Science and AI prototypes into reliable, supportable applications and services, including deployment of Python-based analytics solutions using containerised or cloud-based delivery patterns.
Enable business users and citizen developers by providing technical guidance, templates, and safe pathways from prototype to production.
Partner with global Data Science and engineering teams to apply clean architecture, source control, testing, and deployment standards.
Build and maintain clear user documentation, runbooks, and knowledge resources to ensure sustainment and continuity.
Collaborate closely with technology teams to reduce operational support burden and amplify advanced analytics and AI impact.
Provide EMEA time-zone support for data science, analytics engineering, deployment, troubleshooting, and operational handoff.
Support and modernise legacy analytics solutions, transitioning them into scalable, maintainable patterns using Python, Snowflake, and governed platforms.
Maintain and enhance existing analytics products, including Power BI dashboards and recurring analytical workflows.
What you bring:
Solid experience in data science, software development, or AI-related engineering roles.
Practical experience with Python and data-focused application development.
Familiarity with analytics platforms such as Power BI, Snowflake, or similar data ecosystems.
Experience with cloud-based deployment concepts, DevOps practices, version control systems (e.g. Git), and/or CI/CD pipelines is strongly preferred.
Strong analytical thinking and problem-solving capabilities.
Clear communicator who works effectively with both technical and non-technical team members.
An inclusive, collaborative attitude, we succeed together.
Organised and disciplined approach to delivering high-quality, well-documented work.
Education Preferred:
Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related field, or equivalent practical experience.
Experience:
2–5 years of experience in data science, software engineering, data engineering, or a related technical role preferred.
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