At a Glance
- Tasks: Design and build scalable data pipelines for critical Economics datasets.
- Company: Join Bloomberg, a leader in data-driven technology solutions.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Collaborative environment with mentorship opportunities and cutting-edge technology.
- Why this job: Make an impact by modernising data workflows and enhancing data quality.
- Qualifications: 4+ years in data engineering with strong Python 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 workflow efficiencies and implement technology solutions to enhance our systems, products, and processes.
Our Team: The Economics Data team is responsible for onboarding, modelling, maintaining, and improving Economics datasets that are fit for purpose for our clients. Our data supports workflows across the Bloomberg Terminal, BQL, Enterprise, and other Bloomberg products. We manage macroeconomic, government, survey, forecast, time-series, and vendor‑supplied datasets. Our focus is to deliver Economics data that is accurate, timely, scalable, well‑structured, and ready to use.
What’s the Role: The Economics Data team is looking for a Senior Data Management Professional – Data Engineering to help modernize our data platform and build scalable, resilient data workflows for critical Economics datasets. This role is focused on designing, building, and improving data pipelines, workflow orchestration, automation, monitoring, and technical infrastructure. You will reduce technical debt, modernize legacy processes, and embed quality controls directly into data pipelines and systems. You will work closely with Data, Engineering, Product, and Domain experts to deliver reliable data solutions that improve speed, scalability, observability, and maintainability across the Economics data lifecycle.
We’ll trust you to:
- Build, maintain, and optimize scalable data pipelines for critical Economics datasets.
- Modernize legacy workflows, reduce technical debt, and improve performance, reliability, and maintainability.
- Design automated pipeline controls for validation, monitoring, schema change, exception handling, and data integrity.
- Develop workflow orchestration, alerting, observability, and remediation processes.
- Translate business and client needs into engineering‑ready requirements and scalable technical solutions.
- Partner with Engineering on platform evolution, architecture, tooling, system design, and reliability.
- Apply automation, AI, machine learning, or statistical techniques to improve ingestion, enrichment, validation, and monitoring.
- Own data migrations, workflow redesigns, and technical transformation initiatives.
- Establish best practices for pipeline design, code quality, testing, documentation, version control, and operational handover.
- Influence data modelling, metadata, lineage, and lifecycle management practices from a technical implementation perspective.
- Mentor team members and raise the bar for technical execution, design thinking, and engineering discipline.
You’ll need to have:
- A bachelor’s degree or above in Computer Science, Engineering, Statistics, Mathematics, Economics, Quantitative Finance, or equivalent experience.
- 4+ years of experience designing and building scalable data solutions, ETL pipelines, data workflows, and monitoring frameworks.
- Strong hands‑on experience with Python or similar programming/scripting languages.
- Experience with querying structured, semi‑structured, and unstructured datasets.
- Experience with workflow orchestration, observability, monitoring, alerting, and scalable architecture design.
- Ability to analyze, refactor, and modernize legacy systems.
- Strong understanding of data lifecycle management, data integration, data modelling, data profiling, and data governance.
- Experience building automated controls and reliability frameworks into data pipelines.
- Strong communication skills with the ability to collaborate across Data, Engineering, Product, Vendors, and other stakeholders.
Please note: years of experience are a guide; we will consider applications from all candidates who can demonstrate the skills necessary for the role.
We’d love to see:
- Experience with Economics, macroeconomic, government, survey, forecast, time-series, or vendor‑supplied datasets.
- Bloomberg Terminal, BQL, Enterprise, or Bloomberg data workflow experience.
- Experience productionizing AI, machine learning, anomaly detection, NLP, classification, or LLM‑assisted workflows.
- Experience with cloud platforms, CI/CD, automated testing, version control, metadata management, lineage, or modern DataOps practices.
- Project management experience with Agile delivery, backlog management, JIRA, or similar tools.
- CDMP certification, or progress toward it, is a plus.
If this sounds like you: Apply! If you think we're a good match. We'll get in touch to let you know the next steps!
Senior Data Management Professional - Data Engineer - Economics Data in London employer: Bloomberg
Bloomberg is an exceptional employer that fosters a dynamic and innovative work culture, particularly within the Economics Data team. Employees benefit from a collaborative environment that encourages professional growth through mentorship and exposure to cutting-edge technology, while also enjoying the unique advantage of working in a fast-paced, data-driven industry that values accuracy and efficiency. With a commitment to modernising data workflows and enhancing technical skills, Bloomberg offers meaningful opportunities for those looking to make a significant impact in the world of data management.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Data Management Professional - Data Engineer - Economics Data in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those at Bloomberg or similar companies. A friendly chat can open doors and give you insights that job descriptions just can't.
✨Tip Number 2
Show off your skills! If you've got a portfolio of projects or GitHub repositories, make sure to highlight them during interviews. Real-world examples of your work can set you apart from the crowd.
✨Tip Number 3
Prepare for technical challenges! Brush up on your Python and data pipeline skills, as you might face some hands-on tests. Practising common scenarios can help you feel more confident when it counts.
✨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 Engineer - Economics Data in London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that align with the Senior Data Management Professional role. Highlight your experience with data pipelines, automation, and any relevant projects that showcase your problem-solving skills.
Craft a Compelling Cover Letter:Use your cover letter to tell us why you're passionate about data engineering and how your background fits into our Economics Data team. Be specific about your achievements and how they relate to the job description.
Showcase Your Technical Skills:Don’t forget to mention your hands-on experience with Python and any other programming languages you’re proficient in. We want to see how you've applied these skills in real-world scenarios, especially in building scalable data solutions.
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows us you’re keen on joining our team!
How to prepare for a job interview at Bloomberg
✨Know Your Data Inside Out
Make sure you’re well-versed in the types of datasets mentioned in the job description, like macroeconomic and time-series data. Brush up on your understanding of how these datasets are used in real-world applications, especially within the Bloomberg ecosystem.
✨Showcase Your Technical Skills
Be prepared to discuss your hands-on experience with Python and any other relevant programming languages. Have examples ready that demonstrate your ability to design and build scalable data solutions, as well as your experience with ETL pipelines and workflow orchestration.
✨Demonstrate Problem-Solving Abilities
Think of specific instances where you've identified workflow efficiencies or modernised legacy systems. Be ready to explain your thought process and the impact of your solutions on performance and reliability.
✨Communicate Effectively
Since collaboration is key in this role, practice articulating complex technical concepts in a way that’s easy to understand. Prepare to discuss how you’ve worked with cross-functional teams in the past and how you can bridge the gap between technical and non-technical stakeholders.
