Investment Data Analyst
BH-56286
Posted: 07/08/2026
- Competitive
- United States, United States
- Contract/Freelance
Investment Data Analyst, Investment Data & Analytics
What You’ll Do
What You’ll Do
- Manage daily investment data activities, ensuring timely identification, investigation, and resolution of issues across security reference data, pricing, corporate actions, and fundamental datasets.
- Analyze corporate actions—including mergers, acquisitions, spin-offs, stock splits, and security reclassifications—to determine their implications for positions, performance, and investment analytics.
- Review and investigate data alerts across market data, security mappings, and security characteristic pipelines, separating meaningful exceptions from expected or immaterial anomalies.
- Design, maintain, and enhance investment-ready datasets and data interfaces supporting metrics such as liquidity, issuer market capitalization, industry and sector classifications, and fundamental measures.
- Work extensively with institutional financial data providers, including Bloomberg, Capital IQ, Worldscope, FactSet, MSCI, CRSP, ICE, IBES, and Russell, developing a practical understanding of how their data differs and where potential limitations arise.
- Perform cross-system validation and reconciliation across security master, identifier, and reference data platforms, maintaining accurate relationships between securities, issuers, and historical records using identifiers such as SEDOL, CUSIP, ISIN, and FIGI.
- Work closely with data engineering teams to establish expected data behavior and improve validation rules, transformation processes, exception handling, and monitoring thresholds.
- Partner with quantitative researchers and portfolio managers to translate investment requirements into clear, consistent, and usable data definitions.
- Develop and improve data quality, validation, and reconciliation processes that prioritize investment relevance and downstream portfolio impact rather than technical completeness alone.
- Help advance the investment data platform by contributing to dataset architecture, metadata standards, data models, and workflow design across tools such as Phoenix, Dagster, and Rift.
- Analyze recurring data problems, identify underlying causes, and work with internal engineering teams and external data providers to implement durable solutions.
- Support the broader evolution of investment data processes by identifying opportunities to improve automation, transparency, scalability, and reliability.
- Bachelor’s degree in Finance, Economics, Mathematics, Computer Science, or a related discipline, or equivalent professional experience.
- Significant experience working with investment or financial data within institutional asset management, quantitative investing, or a similarly data-intensive environment.
- Strong practical knowledge of institutional financial data providers and an understanding of how their datasets are applied within investment processes.
- Solid understanding of security reference data, identifier hierarchies, and entity relationships, including SEDOL, CUSIP, ISIN, and FIGI.
- Demonstrated experience analyzing corporate actions and understanding their effects on securities, holdings, historical data, and investment analytics.
- Strong Python and SQL skills for data investigation, analysis, validation, and automation; experience with Pandas, Polars, Dagster, or comparable technologies is a plus.
- Ability to evaluate data issues at both the individual-record and portfolio level, connecting technical discrepancies to potential investment consequences.
- Experience developing, implementing, or maintaining data quality, monitoring, validation, or reconciliation frameworks.
- Familiarity with systematic investment processes, including factor research, portfolio construction, portfolio analytics, and trade execution.
- Strong communication and collaboration skills, with the ability to work effectively across data engineering, quantitative research, investment, and portfolio management functions.
- Comfortable working within complex and evolving data ecosystems, while helping balance operational reliability with platform modernization.
- Strong analytical judgment and the ability to prioritize data issues according to their potential financial, portfolio, and investment impact.
- Naturally curious and detail-oriented, with the persistence to investigate ambiguous data problems and turn one-off fixes into scalable improvements.