At a Glance
- Tasks: Enhance data quality and automate processes for commodities and energy data.
- Company: Join Bloomberg, a leader in data-driven technology and innovation.
- Benefits: Competitive salary, diverse workplace, and opportunities for professional growth.
- Other info: Collaborate with cross-functional teams and drive innovative solutions.
- Why this job: Make a real impact on data quality and efficiency in a dynamic environment.
- Qualifications: 4+ years in data management, strong Python and SQL skills required.
The predicted salary is between 60000 - 80000 € per year.
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 world. In 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.
We are seeking a hands‑on data quality 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.
We’ll 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 optimise 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, standardising 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.
You’ll 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 organisational 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.
We’d 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.
Bloomberg is an equal opportunity employer and we value diversity at our company. We do not discriminate on the basis of age, ancestry, colour, gender identity or expression, genetic predisposition or carrier status, marital status, national or ethnic origin, race, religion or belief, sex, sexual orientation, sexual and other reproductive health decisions, parental or caring status, physical or mental disability, pregnancy or parental leave, protected veteran status, status as a victim of domestic violence, or any other classification protected by applicable law. Bloomberg is a disability inclusive employer. Please let us know if you require any reasonable adjustments to be made for the recruitment process. If you would prefer to discuss this confidentially, please email amer_recruit@bloomberg.net.
Senior Data Management Professional - Data Quality - Commodities Data London, GBR Posted today employer: Bloomberg L.P.
Bloomberg is an exceptional employer, offering a dynamic work culture that fosters innovation and collaboration in the heart of London. With a strong commitment to employee growth, we provide ample opportunities for professional development and skill enhancement, particularly in data management and automation. Our inclusive environment values diversity and encourages employees to contribute to meaningful projects that drive operational efficiency and data quality across commodities and energy sectors.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Data Management Professional - Data Quality - Commodities Data London, GBR Posted today
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with people on LinkedIn. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Prepare for interviews by practising common questions and scenarios related to data quality and automation. We recommend doing mock interviews with friends or using online platforms to get comfortable with your responses.
✨Tip Number 3
Showcase your skills! Create a portfolio or GitHub repository that highlights your projects, especially those involving Python, SQL, or data quality processes. This gives potential employers a tangible look at what you can do.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re genuinely interested in joining our team.
We think you need these skills to ace Senior Data Management Professional - Data Quality - Commodities Data London, GBR Posted today
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that match the job description. Highlight your data management experience, especially in data quality and automation, to show us you're the right fit for the role.
Craft a Compelling Cover Letter:Use your cover letter to tell us why you're passionate about data quality and how your background aligns with our needs. Share specific examples of your achievements in data operations or automation to grab our attention.
Showcase Your Technical Skills:Since we're looking for someone with strong Python and SQL skills, make sure to mention any relevant projects or experiences where you've used these tools. We want to see how you can apply your technical know-how to improve our data processes.
Apply Through Our Website:We encourage you to submit your application through our website. This way, we can ensure your application is reviewed promptly and you get the best chance to shine in front of our hiring team!
How to prepare for a job interview at Bloomberg L.P.
✨Know Your Data Inside Out
Before the interview, dive deep into the specifics of data quality and management. Familiarise yourself with common data issues in commodities and energy datasets, and be ready to discuss how you've tackled similar challenges in the past.
✨Showcase Your Technical Skills
Be prepared to demonstrate your Python and SQL skills during the interview. Consider bringing examples of automation solutions or data validation rules you've implemented, as this will highlight your hands-on experience and problem-solving abilities.
✨Communicate Clearly and Effectively
Since you'll be working with both technical and non-technical stakeholders, practice explaining complex data concepts in simple terms. This will show your ability to bridge the gap between teams and ensure everyone is on the same page.
✨Prepare for Scenario-Based Questions
Expect questions that ask you to solve hypothetical data quality issues or improve existing processes. Think through your approach to root-cause analysis and how you would implement changes, as this will demonstrate your strategic thinking and execution focus.
