At a Glance
- Tasks: Design and develop data workflows and reusable components for efficient data management.
- Company: Join Bloomberg, a leader in innovative data solutions and technology.
- Benefits: Competitive salary, diverse work culture, and opportunities for professional growth.
- Other info: Collaborative environment with a focus on innovation and career advancement.
- Why this job: Make a real impact by optimising data workflows and enhancing AI capabilities.
- Qualifications: Proficiency in Python and SQL with 4+ years in data engineering or related fields.
The predicted salary is between 60000 - 80000 £ per year.
Location: London
Business Area: Data
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 innovative workflow efficiencies, and we implement technology solutions to enhance our systems, products and processes - all while providing customer support to our clients.
Our team: The Bloomberg Data AI group brings innovative AI technologies into Bloomberg’s Data organization while contributing deep financial domain expertise to the development of AI-powered products. We partner closely with stakeholders to align AI innovation with Bloomberg’s strategic objectives, focusing on optimizing data workflows and elevating the quality, intelligence, and usability of the data that drives our products. Our work amplifies the impact of the Data organization by delivering intelligent data solutions and domain-informed systems that enhance the capabilities and competitiveness of Bloomberg’s offerings.
What’s the role? As a Data Engineer on the Shared Infrastructure team, you will play a central role in shaping the foundation for how data workflows are built, scaled, and operated across the organization. You will design and develop shared components, workflow patterns, and developer-facing systems that enable teams to deliver data pipelines with greater consistency, efficiency, and reliability. You will define and implement reusable libraries, templates, and reference architectures for core workflows, including data ingestion, transformation, evaluation, and annotation, establishing common standards that reduce fragmentation and accelerate development across a distributed set of teams. In addition, you will contribute to the evolution of emerging capabilities, such as automated evaluation and LLM-enabled workflows, partnering closely with engineering teams to help integrate and scale these approaches within production environments. This role is critical to advancing a more unified, scalable, and maintainable data ecosystem, shifting the organization from bespoke, one-off solutions toward a coherent, systems-driven approach to data and AI workflow development.
We’ll Trust You To:
- Design and build reusable data pipelines, libraries, and workflow components supporting annotation and evaluation workflows that can be adopted across teams rather than one-off solutions for a single use case.
- Contribute to and integrate with automated evaluation frameworks and LLM-enabled annotation workflows in partnership with AI Engineering teams, creating scalable patterns for data generation, validation, and quality measurement.
- Collaborate on integrations and automation between data systems and LLM services, ensuring solutions are practical, cost-aware, and aligned with engineering constraints.
- Implement monitoring and observability patterns that help teams detect data quality issues, workflow failures, and performance bottlenecks, including those specific to LLM-driven workflows.
- Create reference implementations, templates, and tooling that improve developer experience and make it easier for teams to adopt shared patterns.
- Identify opportunities to reduce manual effort and fragmentation, and implement scalable automation and shared solutions that deliver value across multiple teams.
- Partner closely with engineering teams to translate prototypes into production-ready capabilities, contributing to designs that can be reliably deployed and maintained.
- Work directly with data teams to understand pain points, gather feedback, and drive adoption of shared solutions across the organization.
You’ll Need To Have:
- Strong proficiency in Python and SQL, with experience building data pipelines, automation, and analytics workflows.
- At least 4+ years of professional experience in data engineering, analytics engineering, workflow automation, or a closely related technical role.
- A bachelor’s degree or above in Statistics, Computer Science, Quantitative Finance or other STEM related field or degree-equivalent qualifications.
- Experience working with object stores (e.g., S3), relational databases (e.g., Postgres), data modeling, and pipeline orchestration in production or near-production environments.
- Experience building data validation, monitoring, or observability solutions to ensure data quality and workflow reliability.
- Experience developing reusable components, libraries, or workflows, with an understanding of how to design solutions that can scale across multiple use cases.
- Ability to operate effectively in ambiguous or evolving environments, translating loosely defined problems into practical, scalable solutions.
- Proven ability to work cross-functionally with engineering, data, and product stakeholders to deliver solutions that are both technically sound and broadly usable.
- Strong written and verbal communication skills, including the ability to document systems, define patterns, and explain technical trade-offs clearly.
We’d Love To See:
- Experience with LLM-enabled workflows, annotation pipelines, or AI-driven data processes.
- Familiarity with evaluation frameworks, dataset quality measurement, or approaches to validating model or data outputs.
- Experience improving fragmented or manual workflows through standardization, automation, and reusable tooling.
- Exposure to dataset versioning, workflow instrumentation, and data quality monitoring best practices.
- Experience building shared tools, internal libraries, or systems used across multiple teams.
- Experience partnering with engineering teams to scale prototypes into production-ready systems.
- Familiarity with internal tools such as BBGithub, BCOSv2/BCS, BPaaS, QlikSense, DSP, or similar platforms.
Bloomberg is an equal opportunity employer and we value diversity at our company. We do not discriminate on the basis of age, ancestry, color, 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.
Senior Data Management Professional - Data Engineering (Shared Infrastructure) in London employer: Bloomberg
Bloomberg is an exceptional employer, offering a dynamic work culture that fosters innovation and collaboration in the heart of London. As a Senior Data Management Professional, you will have access to cutting-edge technology and the opportunity to work alongside industry experts, enhancing your skills while contributing to impactful projects. With a strong commitment to employee growth and diversity, Bloomberg provides a supportive environment where your contributions are valued and recognised.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Data Management Professional - Data Engineering (Shared Infrastructure) in London
✨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
Show off your skills! Create a portfolio or GitHub repository showcasing your data engineering projects. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for interviews by practising common data engineering questions and scenarios. Think about how you can demonstrate your problem-solving skills and technical expertise during the chat.
✨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 Engineering (Shared Infrastructure) in London
Some tips for your application 🫡
Tailor Your Application:Make sure to customise your CV and cover letter to highlight your experience in data engineering and how it aligns with the role. We want to see how your skills can contribute to our innovative data solutions!
Showcase Your Technical Skills:Don’t hold back on showcasing your proficiency in Python and SQL! Include specific examples of data pipelines or automation workflows you've built. This is your chance to shine, so let us know what you can do!
Be Clear and Concise:When writing your application, keep it clear and to the point. Use straightforward language to explain your experience and achievements. We appreciate clarity, especially when it comes to technical details!
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’s super easy to do!
How to prepare for a job interview at Bloomberg
✨Know Your Tech Stack
Make sure you’re well-versed in Python and SQL, as these are crucial for the role. Brush up on your experience with data pipelines and automation workflows, and be ready to discuss specific projects where you've applied these skills.
✨Understand the Business Context
Familiarise yourself with Bloomberg's products and how data drives their success. Being able to articulate how your work can enhance data workflows and contribute to the company's objectives will set you apart from other candidates.
✨Showcase Your Problem-Solving Skills
Prepare examples of how you've tackled ambiguous problems in the past. Highlight your ability to translate loosely defined issues into practical solutions, especially in data engineering or workflow automation contexts.
✨Communicate Clearly
Strong communication is key! Be ready to explain technical concepts in a way that’s easy to understand. Practice documenting your thought process and decisions, as this will demonstrate your ability to collaborate effectively with cross-functional teams.
