{"id":2170,"date":"2026-07-01T21:59:41","date_gmt":"2026-07-01T21:59:41","guid":{"rendered":"https:\/\/coderseditor.com\/itjobs\/job\/senior-data-management-professional-data-engineer-commodities-data-quality-2\/"},"modified":"2026-07-01T21:59:41","modified_gmt":"2026-07-01T21:59:41","slug":"senior-data-management-professional-data-engineer-commodities-data-quality-2","status":"publish","type":"job_listing","link":"https:\/\/coderseditor.com\/itjobs\/job\/senior-data-management-professional-data-engineer-commodities-data-quality-2\/","title":{"rendered":"Senior Data Management Professional – Data Engineer – Commodities Data Quality"},"content":{"rendered":"

Senior Data Management Professional – Data Engineer – Commodities Data Quality Overview<\/h2>\n\n\n\n\n\n\n\n\n
Company Name<\/strong><\/td>\nBloomberg<\/td>\n<\/tr>\n
Job Role<\/strong><\/td>\nSenior Data Management Professional – Data Engineer – Commodities Data Quality<\/td>\n<\/tr>\n
Qualifications<\/strong><\/td>\nNot Specified<\/td>\n<\/tr>\n
Category<\/strong><\/td>\nIT Jobs<\/td>\n<\/tr>\n
Job Type<\/strong><\/td>\nFull Time<\/td>\n<\/tr>\n
Location<\/strong><\/td>\nLondon<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Bloomberg is looking for a practical, hands-on data quality and automation specialist to strengthen the reliability, control framework, and operational efficiency of commodities and energy data. The position 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 data pipelines more dependable.<\/p>\n

The wider Data organization at Bloomberg is responsible for delivering data, news, and analytics through technology that works quickly and accurately around the clock. In this environment, the team applies problem-solving and engineering thinking to improve workflows, reduce friction, and build better systems and processes for clients and internal users.<\/p>\n

What you will do<\/h3>\n
    \n
  • Put in place and continuously improve data quality controls for commodities datasets, including market data, reference data, and fundamentals.<\/li>\n
  • Design, maintain, and optimize automated validation checks that assess completeness, accuracy, timeliness, consistency, and schema compliance.<\/li>\n
  • Watch data quality metrics and control outputs, investigate exceptions, and help move issues to resolution in a timely way.<\/li>\n
  • Maintain standards, policies, and KPI reporting for important data domains so that quality expectations are clear and measurable.<\/li>\n
  • Work with data operations colleagues to identify repeated issues and convert them into concrete requirements for process redesign, automation, or engineering fixes.<\/li>\n
  • Improve everyday DataOps practices by reducing manual effort, standardizing how work is done, and reinforcing controls.<\/li>\n
  • Help introduce operational best practices across data workflows, including documentation, testing, change control, and escalation procedures.<\/li>\n
  • Partner with engineering and platform teams to improve observability, alerting, and operational support for critical pipelines.<\/li>\n
  • Build and support automation for validation, exception handling, and workflow efficiency using SQL, Python, or similar tools.<\/li>\n
  • Support imputation controls and rules by validating, flagging, and monitoring imputed values.<\/li>\n
  • Ensure automated processes are properly governed, easy to understand, and aligned with business and control requirements.<\/li>\n
  • Identify opportunities to scale processes and reduce operational risk through targeted automation.<\/li>\n
  • Track data quality issues from initial logging and triage through root-cause analysis, remediation, and final closure.<\/li>\n
  • Support governance across the data lifecycle, from ingestion and normalization through enrichment and distribution.<\/li>\n
  • Coordinate with operations, engineering, and product stakeholders so ownership of issues is clear and follow-through happens.<\/li>\n
  • Produce regular reporting on issue trends, the effectiveness of controls, and progress on remediation.<\/li>\n
  • Act as a daily point of contact for data operations, engineering, and business users on data quality and control topics.<\/li>\n
  • Communicate clearly about priorities, risks, progress, and data issues to the people who need to know.<\/li>\n
  • Bring an execution-focused perspective to broader data quality and automation initiatives.<\/li>\n
  • Support colleagues in delivering larger improvements to processes, controls, and tooling.<\/li>\n<\/ul>\n

    What Bloomberg is looking for<\/h3>\n
      \n
    • At least four years of experience in data management, data operations, or data controls.<\/li>\n
    • Practical experience with data quality checks, exception management, and operational data processes in a complex environment.<\/li>\n
    • Strong Python scripting skills and hands-on SQL ability, or equivalent experience with similar languages, for validation logic, automation, or workflow improvement.<\/li>\n
    • Experience with modern data platforms, workflow tools, or data observability and quality tools.<\/li>\n
    • A proven track record of investigating data issues, carrying out root-cause analysis, and coordinating remediation across teams.<\/li>\n
    • Strong organization and prioritization skills, with the ability to manage several workstreams and finish what you start.<\/li>\n
    • Good communication skills and the ability to work effectively with both technical specialists and non-technical stakeholders.<\/li>\n
    • The company notes that experience length is only a guide and that applicants who can demonstrate the right skills will still be considered.<\/li>\n<\/ul>\n

      Nice to have<\/h3>\n
        \n
      • Experience with commodities, energy, market data, or trading-related datasets.<\/li>\n
      • A STEM background, or experience in technical, quantitative, or data-intensive fields.<\/li>\n
      • Familiarity with DataOps and with how data operations and engineering teams work together to improve reliability and delivery.<\/li>\n
      • Experience in a regulated or tightly controlled data environment.<\/li>\n
      • Exposure to cloud-based data platforms and tools for monitoring pipelines.<\/li>\n
      • Experience helping implement automation, controls, or AI\/ML-based data solutions within a defined validation framework.<\/li>\n<\/ul>\n

        How to apply<\/h3>\n

        If this sounds like a good fit, Bloomberg invites you to apply. The company says it will contact candidates to explain the next steps. It also reiterates that experience guidelines are only a reference point and that applications from people who can demonstrate the required skills are welcome.<\/p>\n

        Additional information<\/h3>\n

        Bloomberg says it provides reasonable adjustments or accommodations for applicants with disabilities. If you need support during the application process, you can request it, including changes to the application process or work procedures, documents in an alternative format, or specialized equipment. Requests can be sent to the regional recruitment email address for the area where you are applying.<\/p>\n

        The company also states that it is an equal opportunity employer and does not discriminate on the basis of protected characteristics. It says it is committed to attracting, retaining, developing, and promoting qualified people fairly and without unlawful bias.<\/p>\n\n

        Degree Requirement:<\/strong> Not Specified<\/p>\n

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