Senior Data Management Professional - Data Engineer - London

Salary: £55,000 - 95,000 per year

Requirements:

• 4+ years of experience in data management, data operations, or data controls
• Experience with data quality checks, exception management, and operational data processes in a complex data environment
• Strong Python scripting skills and practical experience with SQL or similar languages for validation rules, automation, or workflow improvements
• Experience working with modern data platforms, workflow tools, or data observability / quality tooling
• Proven ability to investigate data issues, perform root-cause analysis, and coordinate remediation across teams
• Strong organizational skills with the ability to manage multiple priorities and drive work through to completion
• Effective communication skills with the ability to work across technical and non-technical stakeholders
• Experience with commodities, energy, market data, or trading-related datasets is preferred
• STEM background or experience working in technical, quantitative, or data-intensive disciplines is preferred
• Familiarity with DataOps concepts and how data operations and engineering teams work together to improve reliability and delivery is preferred
• Experience in a regulated or controlled data environment is preferred
• Exposure to cloud-based data platforms and pipeline monitoring tools is preferred
• Experience supporting implementation of automation, controls, or AI/ML-based data solutions within a defined validation framework is preferred

Responsibilities:

• Support the implementation and ongoing enhancement of data quality controls across commodities datasets, including market data, reference data, and fundamentals
• Build, maintain, and optimize automated data quality checks for completeness, accuracy, timeliness, consistency, and schema validation
• Monitor data quality metrics and controls, investigate exceptions, and help drive timely resolution of issues
• Contribute to the maintenance of data quality standards, policies, and KPI reporting for critical data domains
• Work closely with data operations teams to identify recurring data issues and convert them into clear requirements for process improvements, automation, or engineering fixes
• Help improve day-to-day DataOps processes by reducing manual intervention, standardizing workflows, and strengthening controls
• Assist in implementing 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 data pipelines
• Develop and maintain automation solutions for data validation, exception handling, and workflow efficiency using SQL, Python, or similar tools
• Support the implementation of imputation controls and rules, including validation, flagging, and monitoring of imputed values
• Ensure automated processes are well governed, transparent, and aligned with defined business and control requirements
• Identify opportunities to improve scalability and reduce operational risk through targeted automation
• Manage and track data quality issues through logging, triage, root-cause analysis, remediation, and closure
• Support governance of the data lifecycle across ingestion, normalization, enrichment, and distribution processes
• Work with stakeholders across operations, engineering, and product teams to ensure clear ownership and follow-through on data issues
• Prepare regular reporting on issue trends, control effectiveness, and remediation progress
• Act as a key day-to-day partner for data operations, engineering, and business users on data quality and control topics
• Communicate clearly on data issues, priorities, risks, and progress to stakeholders
• Contribute practical input into broader data quality and automation initiatives by bringing an execution-focused perspective
• Support team members in delivering larger process, control, and tooling improvements

Technologies:

• AI
• Cloud
• Support
• Python
• SQL

More:

We are Bloomberg, and our products are fueled by powerful information. In our Data business area, we deliver data, news, and analytics through innovative technology quickly and accurately. This Senior Data Management Professional – Data Engineer role is based in London and sits within our commodities data quality work, focused on improving reliability, control, and operational efficiency across commodities and energy data. We value hands-on problem solving, strong collaboration across data operations, engineering, and business teams, and a delivery-oriented mindset. We also invite candidates who can demonstrate the necessary skills, even if they do not meet every year-of-experience guideline. We encourage you to apply if you think we are a good match, and we will contact you with the next steps.

last updated 35 week of 2026