# Product Manager - Reference Data

**Company:** [Addepar](https://hotfix.jobs/companies/addepar)
**Location:** Edinburgh, United Kingdom
**Role:** Product Management
**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
**Posted:** 2026-06-09

> 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.

## Job Description

## 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.

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