Product Manager - Reference Data

Builds analytical foundations, data-quality processes, reporting infrastructure, and monitoring for reference-data and market-data products. The role requires strong SQL and Python skills, cross-source reporting experience, and familiarity with financial data concepts.

About the job

Responsibilities

Run data investigations, ad hoc queries, and structured analyses to inform prioritization, requirements, and decisions.
Drive data quality analysis for Market Data Feed builds and the security master by profiling vendor data, validating transformations, and identifying anomalies and coverage gaps.
Build and maintain reporting infrastructure covering dataset profitability, vendor licensing costs versus revenue, pipeline health, feed coverage, and commercial activity.
Query across data systems to produce profitability views, budget-spend reporting, commercial pipeline tracking, and dashboards.
Support operational workflows through reconciliation queries, node-matching and security-ID-linking validation, and identification of data-quality issues.
Productize monitoring for market-data pipelines and operational dashboards, triage feed errors, and provide actionable context to operations and R&D.
Use AI-assisted tools to accelerate reference-data product outcomes.
Maintain licensing inventory and dashboards tracking spend against budget, renewal timelines, and licensing events.

Requirements

Prior experience as a data analyst, business analyst, or product analyst, ideally in financial services or a data-intensive SaaS environment.
Strong SQL skills, including confident use of complex queries for investigation.
Experience using AI-assisted tools for analytical work tied to business outcomes.
Experience with Python for data analysis, scripting, and automation; familiarity with pandas, Jupyter, or similar tools.
Ability to build trustworthy reporting across multiple data sources, including Salesforce, finance, and operational data.
Knowledge of, or willingness to learn, financial data concepts such as asset classes, security-level concepts, reference data, and pricing.
Strong written and verbal communication skills, including documenting processes and presenting analysis to diverse audiences.
Dependable, self-starting approach to building analytical solutions.

Skills

SQL, Python, pandas, Jupyter, Salesforce, Data Analysis, Data Quality, Reference Data, Market Data, Data Pipelines, Dashboarding, Data Visualization, Financial Data, Ai-Assisted Tools, Reconciliation