Senior Data Management Professional Data Engineer Commodities Data Quality
Job Location:
Monthly Salary:
Posted on:
17 hours ago
Posted on:
Vacancies:
1 Vacancy
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Job Summary
Senior Data Management Professional - Data Engineer - Commodities Data Quality
Location
London
Business Area
Data
Ref #
Description & Requirements
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 Data we are responsible for delivering this data news and analytics through innovative technology - quickly and accurately. We apply problem-solving skills to identify innovative workflow efficiencies and we implement technology solutions to enhance our systems products and processes.
Whats the role
We are seeking a hands-on Data Quality Engineer and automation professional to help improve the reliability control environment and operational efficiency of commodities and energy data. This role will focus on executing and enhancing data quality processes supporting automation initiatives and partnering closely with data operations engineering and business stakeholders to resolve issues and improve key data pipelines.
This is a delivery-oriented role suited to someone who is strong in implementation and execution with the ability to translate data quality requirements into practical controls monitoring and workflow improvements.
Well trust you to:
- 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.
Youll need to have:
- 4 years experience in data management data operations or data controls
- Experience working 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 implementing 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 communicator with the ability to work across technical and non-technical stakeholders.
*Please note we use years of experience as a guide but we certainly will consider applications from all candidates who are able to demonstrate the skills necessary for the role.
Wed love to see:
- Experience with commodities energy market data or trading-related datasets.
- STEM background or experience working with technical quantitative or data-intensive disciplines.
- Familiarity with DataOps concepts and how data operations and engineering teams work together to improve reliability and delivery.
- Experience in a regulated or controlled data environment.
- Exposure to cloud-based data platforms and pipeline monitoring tools.
- Experience supporting implementation of automation controls or AI/ML-based data solutions within a defined validation framework.
Does this sound like you
Apply if you think were a good match. Well get in touch to let you know what the next steps are!
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Required Experience:
Senior IC
About Company
Bloomberg is the world's primary distributor of financial data and a top news provider of the 21st century. A global information and technology company, we use our dynamic network of data, ideas and analysis to solve difficult problems every day. Our customers around the world rely on ... View more
