Principal Front Office Engineer, Investment Innovation, Integration & Trading

Job Summary

Required Qualifications

• •Excellent problem-solving skills, with the ability to think critically, independently, and act with minimal handholding
• •Effective communication skills, with the ability to clearly articulate complex ideas and analysis to both technical and non-technical stakeholders
• •Strong attention to detail, organization, and the ability to manage multiple tasks and priorities in a fast-paced environment
• •Full-stack development knowledge with a minimum of 8 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)
• •Hands-on experience developing within Bloomberg's BQuant environment
• •Experience writing and maintaining BQL queries, leveraging PyBQL and working with the Bloomberg data ecosystem
• •Experience in front-end development and user experience (UX) design
• •Experience with Pythonic front-end and data visualization libraries (e.g., Plotly, Dash)
• •Experience using Version Control (Git)
• •Experience using Agentic Programming tools (Github Copilot, Claude)
• •Demonstrated ability to partner with business teams to convert AI-generated artifacts (e.g., prototypes, proofs of concept) into production-ready applications
• •Experience bridging the gap between rapid AI-developed prototypes to quality full-stack applications
• •Proven ability to design, build, and scale application systems in data-rich environments including custom AI tools
• •Experience developing applications for investment management firms or, more broadly, financial services
• •Solid SQL skills
• •Familiarity with financial data platforms (such as Bloomberg, FactSet, Aladdin, eFront, Moodys), financial databases, and data manipulation techniques
• •Experience with statistical and time-series data analysis using pythonic libraries (such as Scikit-Learn, SciPy, cvxpy)
• •Solid understanding of financial markets, with foundational knowledge across fixed income, private equity, and private credit investment domains
• •Practical experience in developing and maintaining models, tools, and reports that showcase a deep understanding of quantitative techniques, methods, statistics and econometrics
• •Lead or Senior Engineer
• •Hands-on software architect and builder
• •Must be able to design systems, rapidly iterate on them, and deliver quality solutions that power analytics, reporting, and investment decision-making
• •Must be able to enhance their day-to-day use of AI
• •Must be able to build artifacts in a more technically sound manner
• •Must be able to build robust systems and tools that scale those prototypes into production-ready applications
• •Must be able to serve as the technical lead embedded within the Investment Innovation, Integration & Trading (I3T) team
• •Must be able to drive design and implementation of small-scale proofs of concept and prototypes that support the fixed income portfolio
• •Must be able to improve investment analysis
• •Must be able to develop AI-enabled workflows
• •Must be able to enhance reporting systems & processes
• •Must be able to partner closely with analysts and investment teams
• •Must be able to help them build artifacts in a more technically sound manner
• •Must be able to build robust systems and tools that scale those prototypes into production-ready applications
• •Must be able to drive technical direction without moving into management
• •Must be able to thrive as an individual contributor
• •Must be able to work in a hybrid work arrangement (3 days per week in office)
• •Must be based in Boston

Desired Qualifications

• •Experience in front-end development and user experience (UX) design
• •Experience with Pythonic front-end and data visualization libraries (e.g., Plotly, Dash)
• •Experience writing and maintaining BQL queries, leveraging PyBQL and working with the Bloomberg data ecosystem
• •Hands-on experience developing within Bloomberg's BQuant environment
• •Experience bridging the gap between rapid AI-developed prototypes to quality full-stack applications
• •Experience developing applications for investment management firms or, more broadly, financial services
• •Experience with statistical and time-series data analysis using pythonic libraries (such as Scikit-Learn, SciPy, cvxpy)
• •Familiarity with financial data platforms (such as Bloomberg, FactSet, Aladdin, eFront, Moodys), financial databases, and data manipulation techniques
• •Solid understanding of financial markets, with foundational knowledge across fixed income, private equity, and private credit investment domains
• •Practical experience in developing and maintaining models, tools, and reports that showcase a deep understanding of quantitative techniques, methods, statistics and econometrics