Front Office Engineer

W2 candidates only. Must be authorized to work in the U.S without employer sponsorship now or in the future. This Boston-based role has a hybrid work arrangement (3 days per week in office). Onsite interviews required.

We are seeking a highly skilled and motivated Principal Front-Office Engineer to join our prestigious investment firm. As a Principal Front-Office Engineer, you will serve as the technical lead embedded within the Risk team, driving design and implementation of small-scale applications and proof of concepts that will improve risk analysis, develop AI-enabled workflows, and enhance reporting systems & processes. You will work closely with risk analysts and investment teams, but your primary focus will be building robust systems and tools that power risk infrastructure, analytics, and decision-ready reporting. We re looking for a hands-on software architect and builder who has experience designing systems, rapidly iterating over them and delivering them across the finish line.

This role is ideal for a Lead or Senior Engineer who thrives as an individual contributor and wants to drive technical direction without moving into management.

Qualifications

• Effective communication skills, with the ability to clearly articulate complex ideas and analysis to both technical and non-technical stakeholders.
• Minimum of 5 years professional experience programming in Python demonstrating the ability to write efficient and robust code able to process and analyze large financial datasets.
• Experience with key Python Libraries (pandas, NumPy) required.
• Experience in front-end development and user experience (UX) design required; experience with Pythonic front-end and data visualization libraries (e.g., Plotly, Dash) preferred.
• Experience using Agentic Programming tools (Github Copilot, Claude) required.
• Strong SQL skills required with a familiarity of financial data platforms (such as Bloomberg, FactSet, Aladdin, eFront, Moodys), financial databases, and data manipulation techniques preferred. Experience with statistical and time-series data analysis using pythonic libraries (such as Scikit-Learn, SciPy, cvxpy) is preferred.
• Experience working on an investment team or company.
• Practical experience in developing and maintaining models, tools, and reports that showcase a deep understanding of quantitative techniques, methods, statistics and econometrics.