Senior Data Management Professional – Data Engineer – Commodities Data Quality

Senior Data Management Professional – Data Engineer – Commodities Data Quality Overview

Bloomberg’s data organization is looking for a practical, hands-on data quality and automation specialist to help strengthen the reliability, control framework, and operational efficiency of commodities and energy data. The role sits within the Data business area in London and is focused on delivery: turning data quality needs into working controls, monitoring, and workflow improvements that make key pipelines more dependable and easier to run.

The team’s work supports Bloomberg’s broader mission of powering products with high-quality information, delivered quickly and accurately through technology. In this role, you would work closely with data operations, engineering, platform, and business stakeholders to identify issues, improve processes, and reduce manual effort across important data flows.

What you will do

• Implement and continuously improve data quality controls across commodities datasets, including market data, reference data, and fundamentals.
• Design, build, and maintain automated checks that assess completeness, accuracy, timeliness, consistency, and schema validity.
• Monitor control outputs and data quality metrics, investigate exceptions, and help drive issues through to timely resolution.
• Maintain data quality standards, policy documentation, and KPI reporting for critical data domains.
• Work with data operations teams to identify recurring issues and translate them into process changes, automation opportunities, or engineering requirements.
• Reduce manual intervention in day-to-day DataOps work by standardizing workflows and strengthening control points.
• Support operational best practices across data workflows, including documentation, testing, change management, and escalation procedures.
• Partner with engineering and platform teams to improve observability, alerting, and operational support for key pipelines.
• Develop and support automation for validation, exception handling, and workflow efficiency using SQL, Python, or similar tools.
• Help implement imputation controls and rules, including validation, flagging, and monitoring of imputed values.
• Ensure automated processes are well governed, transparent, and aligned with business and control requirements.
• Look for opportunities to improve scalability and reduce operational risk through targeted automation.
• Track data quality issues from logging and triage through root-cause analysis, remediation, and closure.
• Support governance across the full data lifecycle, including ingestion, normalization, enrichment, and distribution.
• Coordinate with operations, engineering, and product stakeholders so ownership of issues is clear and follow-through happens.
• Prepare regular reporting on issue trends, control effectiveness, and remediation progress.
• Act as a day-to-day partner for data operations, engineering, and business users on data quality and control topics.
• Communicate clearly about priorities, risks, progress, and data issues to a range of stakeholders.
• Bring an execution-focused perspective to broader data quality and automation initiatives.
• Support colleagues in delivering larger process, control, and tooling improvements.

What the team is looking for

• At least four years of experience in data management, data operations, or data controls.
• Practical experience with data quality checks, exception management, and operational data processes in a complex environment.
• Strong Python scripting skills and hands-on SQL ability, or equivalent language experience, for validation logic, automation, and workflow improvements.
• Experience with modern data platforms, workflow tools, or data observability and quality tooling.
• Proven ability to investigate data issues, carry out root-cause analysis, and coordinate remediation across teams.
• Strong organization and prioritization skills, with the ability to manage multiple workstreams and complete tasks reliably.
• Effective communication skills and the ability to work with both technical and non-technical stakeholders.
• The company notes that years of experience are only a guide and that candidates who can demonstrate the right skills will still be considered.

Helpful additional experience

• Experience with commodities, energy, market data, or trading-related datasets.
• A STEM background, or experience in technical, quantitative, or data-intensive disciplines.
• Familiarity with DataOps concepts and with how data operations and engineering teams collaborate to improve reliability and delivery.
• Experience working in a regulated or tightly controlled data environment.
• Exposure to cloud-based data platforms and pipeline monitoring tools.
• Experience supporting automation, controls, or AI/ML-based data solutions within a defined validation framework.

How to apply

If this sounds like a good fit, you are encouraged to apply. Bloomberg says it will contact candidates to explain the next steps after an application is submitted. The posting also notes that applicants should feel free to apply even if they do not match every point exactly, as the company will consider people who can demonstrate the necessary skills.

Additional information

Bloomberg provides reasonable accommodations or adjustments for applicants with disabilities. If you need support during the application process, you can request it by email for the relevant region. Examples include changes to the application process or work procedures, documents in an alternative format, or specialized equipment. The company may share your information with a third-party accommodations provider for the purpose of helping with your request.

Bloomberg also states that it is an equal opportunity employer and does not discriminate in employment. It says it is committed to equal access and to hiring, retaining, developing, and promoting the most qualified people regardless of protected characteristics.

Applicants are also directed to Bloomberg’s podcast series for a closer look at the company’s culture, values, and employees.

Degree Requirement: Not Specified

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