At a Glance
- Tasks: Design and build scalable data pipelines for critical Economics datasets.
- Company: Join Bloomberg, a leader in data-driven technology and innovation.
- Benefits: Competitive salary, diverse workplace, and opportunities for professional growth.
- Other info: Collaborative environment with mentorship opportunities and career advancement.
- 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.
Location: London
Business Area: Data
Ref #10051525
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 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 modernise 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, modernise 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 optimise scalable data pipelines for critical Economics datasets.
- Modernise 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 analyse, refactor, and modernise 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.
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 productionising 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 towards it, is a plus.
If this sounds like you: We encourage you to apply. If you feel you are a strong fit, please submit your application through our career portal.
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, nationality 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 Engineer - Economics Data London, GBR employer: Bloomberg L.P.
Bloomberg is an exceptional employer, offering a dynamic work environment in the heart of London where innovation and collaboration thrive. Employees benefit from a culture that prioritises professional growth, with ample opportunities for mentorship and skill development in cutting-edge data technologies. The company's commitment to diversity and inclusion, alongside its focus on modernising data solutions, makes it an attractive place for those seeking meaningful and impactful careers in data engineering.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Data Management Professional - Data Engineer - Economics Data London, GBR
✨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 refer you directly.
✨Tip Number 2
Prepare for interviews by practising common questions and showcasing your problem-solving skills. Use real-life examples from your experience to demonstrate how you've tackled challenges in data management and engineering.
✨Tip Number 3
Don’t just apply and wait! Follow up on your applications after a week or so. A quick email can show your enthusiasm and keep you on their radar. Plus, it’s a great way to ask if they need any more info from you.
✨Tip Number 4
Check out our website for the latest job openings and apply directly through there. It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are proactive!
We think you need these skills to ace Senior Data Management Professional - Data Engineer - Economics Data London, GBR
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the role of Senior Data Management Professional. Highlight your experience with data pipelines, automation, and any relevant projects that showcase your skills in data engineering.
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about data management and how your background aligns with the Economics Data team's goals. Be specific about your achievements and how they relate to the job.
Showcase Your Technical Skills:Don’t forget to highlight your technical skills, especially in Python and data lifecycle management. Mention any tools or frameworks you’ve used that are relevant to the role, as this will show us you’re ready to hit the ground running.
Apply Through Our Website:We encourage you to apply through our career portal. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it’s super easy to do!
How to prepare for a job interview at Bloomberg L.P.
✨Know Your Data Inside Out
Make sure you’re well-versed in the types of datasets mentioned in the job description, especially those related to economics. Brush up on your knowledge of macroeconomic data, time-series analysis, and any relevant tools like Bloomberg Terminal. This will show that you understand the core of what the team does.
✨Showcase Your Technical Skills
Prepare to discuss your hands-on experience with Python or similar programming languages. Be ready to share specific examples of how you've designed and built scalable data solutions or ETL pipelines. Highlight any projects where you’ve modernised legacy systems or implemented automated controls.
✨Demonstrate Problem-Solving Abilities
Since the role involves identifying workflow efficiencies, think of examples where you’ve successfully solved complex data issues. Be prepared to explain your thought process and the impact of your solutions on previous projects. This will illustrate your analytical skills and ability to improve processes.
✨Communicate Effectively
Strong communication skills are key for this role. Practice articulating technical concepts in a way that non-technical stakeholders can understand. Prepare to discuss how you’ve collaborated with cross-functional teams in the past, as this will be crucial for working closely with Data, Engineering, and Product teams.
