Quantitative Developer, IDEA Team

About the position The Team – Investment Data Engineering & Analytics (IDEA) The Investment Data Engineering & Analytics (IDEA) team sits within the Investment Platform (IP) COO organization. The IP COO group is responsible for enabling the Investment Platform to achieve its growth and efficiency goals by creating scalable centers of excellence, aligning with business needs, and integrating business and technology strategies. As data continues to grow in importance as an enabler of the investment process, the IDEA team is responsible for evolving the firm’s research data and analytics platform. The team partners closely with investors, technologists, and enterprise data functions to build and sustain a platform that provides seamless access to a library of foundational research data and analytics across asset classes. The Position We are seeking a Quantitative Developer to join the IDEA team and help design, build, and extend our central research data platform. This individual will work primarily in Python and modern cloud data technologies to build the full stack of investment data and analytics: from transforming raw vendor and internal data into clean, well-modeled, investment-ready datasets to tools that power fundamental and systematic research. The ideal candidate combines strong Python engineering skills, a deep interest in data modeling and architecture, and a practical understanding of investment data and how investors use it. This person is energized by building in a dynamic environment, comfortable with ambiguity, and motivated by the opportunity to create structure from complexity. This role will work closely with both fundamental and quantitative investors and researchers, technology partners, and enterprise data teams.

Responsibilities

• Design and implement robust data models for securities, issuers, fundamentals, time series, and analytics across multiple asset classes (e.g., equity, fixed income, macro).
• Develop and maintain Python-based libraries and services that provide consistent, well-documented access to research data and analytics.
• Partner with data engineering to ensure upstream data and pipelines support analytics needs.
• Collaborate with investors and quantitative researchers to understand their workflows and translate requirements into scalable data and tooling solutions.
• Contribute to the rationalization of data vendors and the convergence of legacy data stores into a cohesive, central platform capability.
• Implement and enhance data and analytics quality controls, monitoring, and documentation to promote trust in both the data and the analytics built on top of it.
• Participate in code reviews, design discussions, and standards-setting to ensure high engineering quality and reusability across the platform.
• Proactively identify opportunities to improve performance, usability, and reliability of the platform, and drive initiatives from concept through to adoption.

Requirements

• Strong hands-on experience with Python for data-intensive applications, including use of common libraries (e.g., pandas, polars, numpy) and building testable, maintainable, production-quality code.
• Solid understanding of data modeling concepts, particularly for time-series and reference data (e.g. slowly changing dimensions, point-in-time and bi-temporal data).
• Proficiency with SQL and experience working with large datasets in modern data platforms (e.g., Snowflake, cloud data warehouses, data lakes) and open-source formats such as parquet.
• Strong software engineering fundamentals: version control (git), code reviews, unit/integration testing, logging, and documentation.
• Working knowledge of investment data, including:
• Security master and symbology (e.g., issuer vs. security identifiers, vendor symbologies).
• Fundamental data (e.g., financial statements, estimates), pricing and returns, benchmarks, and basic risk/portfolio concepts.
• Familiarity with the practical use of data in investment workflows such as screening, backtesting, portfolio analysis, factor, and performance / attribution concepts.
• 3-7 years of professional experience as a quantitative developer, quantitative analyst, or research platform/analytics engineer in asset management, a hedge fund, or a similarly data-driven financial environment.
• Bachelor’s degree in Computer Science, Engineering, Mathematics, Statistics, or a related quantitative field, or equivalent professional experience.

Benefits

• retirement plan
• health and wellbeing
• dental
• vision
• pharmacy coverage
• health savings account
• flexible spending accounts and commuter program
• employee assistance program
• life and disability insurance
• adoption assistance
• back-up childcare
• tuition/CFA reimbursement
• paid time off (leave of absence, paid holidays, volunteer, sick and vacation time)

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