Principal Front-Office Engineer - Investments Technology (Private Markets)

Working directly with the PSM team and the broader Private Markets organization, you will focus on enhancing day-to-day AI use cases, helping turn prototypes and POC artifacts into robust systems and tools that scale into production-ready applications. Embed with the PSM team to understand portfolio strategy, pacing, liquidity, and performance workflows, and translate them into well-scoped, iteratively delivered tools. Design and build full-stack Python applications, data pipelines, and decision-ready reports on enterprise platforms (GitHub, CI/CD via GitHub Actions, Snowflake, Azure AD-secured web apps). Help analysts take AI-generated prototypes and proofs of concept and harden them into reliable, maintainable, institutional-grade applications. Implement output validation, documented assumptions and limitations, and basic runtime logging for decision-support tools; provide best-effort operational support and remediate vulnerabilities within enterprise SLAs. Maintain clean, well-organized repositories: consistent project structure, automated linting and formatting, semantic version bumps and tagged releases, and up-to-date documentation so every solution stays maintainable and auditable. Contribute to and reuse shared utilities (connectors, authentication helpers, report distribution) in collaboration with other embedded engineers. 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 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 Version Control (Git) required; experience with CI/CD workflows (e.g., GitHub Actions) and cloud deployment preferred. 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 institutional-grade, production-ready applications. Experience bridging the gap between rapid AI-assisted development and enterprise-quality software delivery 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. Experience with Snowflake is a plus. Familiarity with private markets data platforms (such as eFront, PitchBook, DealCloud) and data manipulation techniques strongly preferred. Experience with statistical and time-series data analysis using pythonic libraries (such as Scikit-Learn, SciPy, cvxpy) is preferred; experience with Monte Carlo or scenario-based cash-flow modeling is a plus. Solid understanding of private markets, including fund structures, capital calls and distributions, J-curve dynamics, commitment pacing, and performance measurement (IRR, MOIC/TVPI, DPI, PME); foundational knowledge across private equity, private credit, real estate, and infrastructure. Practical experience in developing and maintaining models, tools, and reports that showcase a deep understanding of quantitative techniques, methods, statistics and econometrics. 8+ years of experience Bachelor's degree or equivalent experience