Senior Data Management Professional - Data Quality - Economics Data

Senior Data Management Professional - Data Quality - Economics Data

Location:

London

Business Area:
Data

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Bloomberg runs on data. Our products are fueled by powerful information. We combine data and context to paint the whole picture for our clients, around the clock - from around the world. In Data, we are responsible for delivering this data, news, and analytics through innovative technology - quickly and accurately. We apply problem‑solving skills to identify workflow efficiencies and implement technology solutions to enhance our systems, products, and processes.

The Economics Data team is responsible for onboarding, modelling, maintaining, and improving Economics datasets that are fit for purpose for our clients. Our data supports workflows across the Bloomberg Terminal, BQL, Enterprise, and other Bloomberg products. We manage macroeconomic, government, survey, forecast, time‑series, and vendor‑supplied datasets. Our focus is to deliver Economics data that is accurate, timely, complete, transparent, and ready to use.

The Economics Data team is looking for a Senior Data Management Professional – Data Quality to define and drive the quality strategy for Economics data products.

This role is focused on setting quality standards, defining fit‑for‑purpose metrics, strengthening controls, improving issue management, and ensuring data quality is measured transparently across tools, processes, and datasets.

You will work closely with Data, Engineering, Product, Vendors, and Domain teams to ensure Economics datasets meet client expectations, support commercial priorities, and are governed through clear controls, ownership, and measurable outcomes.

• Define and own the data quality vision, strategy, and roadmap for Economics datasets.
• Set fit‑for‑purpose quality metrics, SLAs, targets, and standards aligned to client, product, commercial, and regulatory needs.
• Design data quality controls across completeness, accuracy, timeliness, consistency, schema change, anomaly detection, and data integrity.
• Use data profiling, root‑cause analysis, and trend analysis to identify quality risks and drive sustainable remediation.
• Own the data quality issue management framework, including logging, triage, prioritization, accountability, remediation tracking, and closure.
• Partner with Engineering to translate quality needs into pipeline controls, monitoring, tooling, observability, and automation requirements.
• Represent Economics Data in lifecycle governance, policy implementation, and quality framework discussions.
• Define guardrails for automated or AI‑assisted quality workflows, including imputation, validation, exception handling, and downstream flagging.
• Improve transparency of tools, processes, and data health through dashboards, reporting, and regular communication to senior partners.
• Work with Vendors, Product, Engineering, and Data teams to resolve quality issues at source and prevent recurrence.
• Influence data governance, metadata, lineage, data modelling, and lifecycle management practices across Economics datasets.
• Promote a culture of accountability, continuous improvement, automation, and client‑focused quality.

• A bachelor’s degree or above in Economics, Statistics, Computer Science, Mathematics, Engineering, Quantitative Finance, or equivalent experience.
• 4+ years of experience in data management, data quality, data operations, data governance, or data product ownership.
• Solid experience defining quality metrics, SLAs, controls, KPIs, issue management processes, and remediation frameworks.
• Experience working across the full data lifecycle, including ingestion, normalization, enrichment, modelling, quality control, distribution, and monitoring.
• Strong analytical skills, including data profiling, root‑cause analysis, trend analysis, and evidence‑based decision‑making.
• Technical grounding in Python, SQL, data analysis, data visualization, or similar tools used to assess and improve data quality.
• Experience translating business and client needs into clear requirements for Engineering, Product, vendors, or operational teams.
• Good…