Senior Data Management Professional – Data Engineer – Commodities Data Quality

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

Bloomberg is looking for a practical, delivery-focused data quality and automation specialist to strengthen the reliability, control framework, and operational efficiency of its commodities and energy data. The role sits within the Data business area in London and is centered on improving how critical datasets are validated, monitored, governed, and maintained across the data lifecycle. This position is well suited to someone who enjoys hands-on implementation, can turn data quality needs into workable controls and automation, and is comfortable partnering with operations, engineering, and business teams to resolve issues and improve pipelines.

What you will do

• Put in place and continuously improve data quality controls for commodities datasets, including market data, reference data, and fundamentals.
• Design, build, maintain, and optimize automated checks that assess completeness, accuracy, timeliness, consistency, and schema validity.
• Monitor quality metrics and control outputs, investigate exceptions, and help drive issues through to timely resolution.
• Support the upkeep of data quality standards, policies, and KPI reporting for key data domains.
• Work with data operations teams to identify recurring issues and translate them into requirements for process changes, automation, or engineering fixes.
• Improve day-to-day DataOps activity by reducing manual intervention, standardizing workflows, and reinforcing control points.
• Help establish operational best practices across data workflows, including documentation, testing, change management, and escalation handling.
• Partner with engineering and platform teams to improve observability, alerting, and operational support for important data pipelines.
• Develop and maintain automation for validation, exception handling, and workflow efficiency using SQL, Python, or similar tools.
• Support the design and implementation of 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.
• Identify opportunities to improve scale and reduce operational risk through targeted automation.
• Track data quality issues from initial logging and triage through root-cause analysis, remediation, and final closure.
• Support governance of the data lifecycle across ingestion, normalization, enrichment, and distribution.
• Coordinate with stakeholders across operations, engineering, and product so ownership is clear and follow-through happens on data issues.
• Produce 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 different stakeholder groups.
• Bring an execution-oriented perspective to broader data quality and automation initiatives.
• Support colleagues in delivering larger process, control, and tooling improvements.

What Bloomberg is looking for

• At least four years of experience in data management, data operations, or data controls.
• Experience working with data quality checks, exception handling, and operational data processes in a complex environment.
• Strong Python scripting skills and practical SQL ability, or equivalent language experience, for validation logic, automation, or workflow improvements.
• Experience with modern data platforms, workflow tools, or data observability and quality tooling.
• A proven ability to investigate data issues, carry out root-cause analysis, and coordinate remediation across teams.
• Strong organizational skills and the ability to manage several priorities while delivering work to completion.
• Good communication skills and the ability to work effectively with both technical and non-technical stakeholders.
• Bloomberg notes that experience length is only a guide and that it will consider candidates who can demonstrate the necessary skills, even if they do not match the stated years exactly.

Nice-to-have experience

• Experience with commodities, energy, market data, or trading-related datasets.
• A STEM background, or experience in technical, quantitative, or data-intensive fields.
• Familiarity with DataOps practices and how data operations and engineering teams work together to improve reliability and delivery.
• Experience 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.

Team and environment

This role is part of Bloomberg’s Data organization, where the company focuses on delivering data, news, and analytics through technology quickly and accurately. The team works on improving workflows, strengthening controls, and building practical solutions that make data operations more reliable and efficient. The position involves close collaboration with data operations, engineering, platform, product, and business stakeholders.

How to apply

If the role sounds like a good fit, you are encouraged to apply. Bloomberg says it will follow up with next steps after reviewing applications. The posting also invites candidates to explore Bloomberg’s podcast series to learn more about the company’s culture, values, and people.

Additional information

Bloomberg provides reasonable accommodations or adjustments for applicants with disabilities. Support can include changes to the application process or work procedures, alternate document formats, or specialized equipment. Applicants can request accommodation by contacting the relevant regional recruitment email address for the Americas, EMEA, or APAC. Bloomberg also states that it is an equal opportunity employer and does not discriminate on the basis of protected characteristics.

Degree Requirement: Not Specified

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