At a Glance
- Tasks: Lead the charge in ensuring top-notch quality for economics datasets.
- Company: Join Bloomberg, a leader in data-driven solutions and innovation.
- Benefits: Competitive salary, health benefits, and opportunities for professional growth.
- Other info: Dynamic team environment with a focus on continuous improvement and innovation.
- Why this job: Make a real impact on data quality that drives global financial decisions.
- Qualifications: Degree in relevant fields and 4+ years in data management or quality.
The predicted salary is between 60000 - 80000 £ per year.
Bloomberg runs on data. Our products are fueled by powerful information. The Economics Data team is responsible for onboarding, modelling, maintaining, and improving economics datasets that are fit for purpose for our clients. Data supports workflows across the Bloomberg Terminal, BQL, Enterprise, and other Bloomberg products.
The Economics Data team is looking for a Senior Data Management Professional – Data Quality to define and drive the quality strategy for economics data products. This role focuses on setting quality standards, defining fit‑for‑purpose metrics, strengthening controls, improving issue management, and ensuring data quality is measured transparently across tools, processes, and datasets.
Responsibilities
- Define and own the data quality vision, strategy, and roadmap for economics datasets.
- Set fit‑for‑purpose quality metrics, SLAs, targets, and standards aligned to client, product, commercial, and regulatory needs.
- Design data quality controls across completeness, accuracy, timeliness, consistency, schema change, anomaly detection, and data integrity.
- Use data profiling, root‑cause analysis, and trend analysis to identify quality risks and drive sustainable remediation.
- Own the data quality issue management framework, including logging, triage, prioritization, accountability, remediation tracking, and closure.
- Partner with engineering to translate quality needs into pipeline controls, monitoring, tooling, observability, and automation requirements.
- Represent economics data in lifecycle governance, policy implementation, and quality framework discussions.
- Define guardrails for automated or AI‑assisted quality workflows, including imputation, validation, exception handling, and downstream flagging.
- Improve transparency of tools, processes, and data health through dashboards, reporting, and regular communication to senior partners.
- Work with vendors, product, engineering, and data teams to resolve quality issues at source and prevent recurrence.
- Influence data governance, metadata, lineage, data modelling, and lifecycle management practices across economics datasets.
- Promote a culture of accountability, continuous improvement, automation, and client‑focused quality.
Required Qualifications
- Bachelor’s degree or above in economics, statistics, computer science, mathematics, engineering, quantitative finance, or equivalent experience.
- 4+ years of experience in data management, data quality, data operations, data governance, or data product ownership.
- Solid experience defining quality metrics, SLAs, controls, KPIs, issue‑management processes, and remediation frameworks.
- Experience working across the full data lifecycle, including ingestion, normalization, enrichment, modelling, quality control, distribution, and monitoring.
- Strong analytical skills, including data profiling, root‑cause analysis, trend analysis, and evidence‑based decision‑making.
- Technical grounding in Python, SQL, data analysis, data visualization, or similar tools used to assess and improve data quality.
- Experience translating business and client needs into clear requirements for engineering, product, vendors, or operational teams.
- Good understanding of data governance, data lifecycle management, data modelling, metadata, and data integrity principles.
- Superb communication and stakeholder management skills, with the ability to influence senior partners and align distributed teams.
- Ability to operate through ambiguity, set direction, prioritize effectively, and drive measurable quality improvements.
Preferred Qualifications
- Experience with economics, macroeconomic, government, survey, forecast, time‑series, or vendor‑supplied datasets.
- Bloomberg Terminal, BQL, Enterprise, or Bloomberg data workflow experience.
- Experience designing quality strategies in complex, regulated, or high‑control environments.
- Experience using AI, machine learning, anomaly detection, statistical methods, or automation to improve data quality workflows.
- Experience defining validation frameworks for automated, AI‑assisted, or imputed data outputs.
- Experience with observability tools, workflow orchestration, issue tracking, data catalogues, lineage, metadata management, or modern DataOps practices.
- Project management experience with Agile delivery, backlog management, JIRA, QlikSense, or similar tools.
- Understanding of causal inference.
- CDMP certification, or progress toward it, is a plus.
Senior Data Management Professional - Data Quality - Economics Data employer: Bloomberg
Bloomberg is an exceptional employer, offering a dynamic work environment in the heart of London where innovation and data excellence thrive. With a strong focus on employee growth, Bloomberg provides ample opportunities for professional development and collaboration across diverse teams, fostering a culture of accountability and continuous improvement. Employees benefit from competitive compensation, comprehensive benefits, and the chance to work with cutting-edge technology in a globally recognised firm that values quality and client satisfaction.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Data Management Professional - Data Quality - Economics Data
✨Get Involved in Data Science Meetups
Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like Bloomberg!
✨Show Off Your Projects
Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like Senior Data Management Professional - Data Quality - Economics Data at Bloomberg.
✨Leverage Professional Networks
Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Bloomberg.
✨Apply Directly through Our Website
When you find a suitable opening like Senior Data Management Professional - Data Quality - Economics Data at Bloomberg, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!
We think you need these skills to ace Senior Data Management Professional - Data Quality - Economics Data
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Bloomberg, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Bloomberg. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Bloomberg
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Bloomberg!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
