Principal Front Office Engineer, Investment Innovation, Integration & Trading

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, Num Py) required Hands-on experience developing within Bloomberg's BQuant environment is strongly preferred. Experience writing and maintaining BQL queries, leveraging PyBQL and working with the Bloomberg data ecosystem is strongly preferred 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 Version Control (Git) required. Experience using Agentic Programming tools (Github Copilot, Claude) required. 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 is highly valued. 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 is a strong plus. Strong SQL skills required. Familiarity with financial data platforms (such as Bloomberg, Fact Set, Aladdin, e Front, Moodys), financial databases, and data manipulation techniques strongly preferred. Experience with statistical and time-series data analysis using pythonic libraries (such as Scikit-Learn, Sci Py, cvxpy) is preferred. 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