Reference Data Product Analyst

The Reference Data Product Analyst supports the reference data needs of Firmwide Planning & Analysis (P&A) across forecasting, reporting, and analytics.  This role serves as a bridge between P&A stakeholders and data/technology teams, ensuring reference data requirements, quality, controls, lineage, and delivery are fit for purpose, well-governed, and scalable.  The analyst is responsible for day-to-day production support and for driving strategic initiatives related to P&A reference data domains, including Organization, Product, Balance Sheet Group, and others.
 

Key Responsibilities

• Elicit, document, and validate business requirements for reference data enhancements and defect remediation.
• Translate needs into clear user stories, functional requirements, acceptance criteria, and process flows.
• Support agile delivery (backlog refinement, sprint activities, release readiness) and coordinate UAT (test cases, execution, defect triage).
• Perform monthly operational tasks including data validation checks, exception reviews, control evidence, and recurring reporting/metrics.
• Investigate data issues using SQL; partner with technology and stakeholders to drive root-cause remediation.
• Identify recurring break patterns and drive root-cause analysis with upstream providers and technology.
• Maintain domain documentation (definitions, hierarchies, mappings, procedures/runbooks) aligned to governance standards.
• Collaborate across Finance/Risk/Operations/Technology to standardize taxonomy and ensure consistent consumption.

Required Qualifications

• Bachelor’s degree or equivalent experience.
• 2–4 years’ experience in business analysis, data analysis, or data operations (reference/master data preferred).
• Strong requirements writing, stakeholder management, and testing/UAT support experience.
• Working knowledge of SQL for data validation and troubleshooting.

Preferred Qualifications

• Databricks experience (preferred) and familiarity with large-scale data platforms.
• Knowledge of reference data concepts (taxonomies, hierarchies, mappings) and data quality controls/metrics.
• Exposure to data quality/control frameworks (metrics, SLAs, reconciliations, exceptions).
• Experience in agile delivery environments and tooling (e.g., Jira/Confluence or similar).