Senior Software Engineer - Data Technologies, Non-Securitised Data - Macro & Industries
Bloomberg
Software Engineering
London, UK
Posted on Mar 19, 2026
Bloomberg is foremost a data company. Data is at the heart of everything we do; we collect it, cleanse it, enrich it, derive it, validate it, and make it available to our clients. This data is vast and varied and critical not only to our success but to that of our diverse global client base, and we continuously challenge ourselves to do this better and faster.
You'll need to have:
We'd love to see:
We are a team of software engineers who build and maintain data pipelines for Bloomberg's Macro business areas, including Economics, Energy Transition, Physical Assets & Geo, Commodities & Carbon, and Macroeconomic Analysis. Working in close partnership with Bloomberg's Data department, we ensure that critical data and metadata flows reliably from source systems into Bloomberg's products, spanning ingestion, transformation, standardisation, enrichment, and downstream integration.
Our engineers take ownership of their projects end-to-end, managing both technical delivery and stakeholder relationships. We also partner closely with infrastructure teams, contributing application-level insights that help guide platform improvements and influence infrastructure strategy. The breadth of domains we support gives you exposure to a broad and varied problem space.
We care deeply about engineering craft. We build reusable components and shared libraries that solve cross-domain problems, abstracting away complexity so that common patterns are handled well in one place rather than duplicated across pipelines. We look for recurring challenges and invest in building tools that improve reliability, reduce risk, and make our teams more productive. This mindset means we are always looking for opportunities to raise the bar on performance, code quality, and maintainability.
We are actively exploring how agentic and generative AI can augment our data workflows to improve data coverage and quality. We are also contributing to Bloomberg's semantic data initiatives, helping define how data flows into Bloomberg's enterprise knowledge graph.
We value incremental delivery over big-bang releases. Getting our work into the hands of users early helps ensure we are building what the business needs. We foster a culture of psychological safety, collaboration, and continuous learning, where it's safe to ask questions, challenge ideas, and support each other to deliver under pressure.
We'll trust you to:
- Take ownership of projects and drive them from design through to delivery
- Build robust, scalable data pipelines that process large volumes of complex data reliably
- Identify recurring problems across domains and build reusable solutions that benefit multiple teams
- Develop strong working relationships with engineering peers, data teams, and business stakeholders
- Champion engineering best practices, writing well-tested, maintainable, and high-quality code
- Deliver incrementally in a fast-paced environment, prioritising thoughtfully across competing workstreams
You'll need to have:
- Strong backend experience with Python
- A degree in Computer Science, Engineering, Mathematics, a similar field of study, or equivalent work experience
- Experience building and maintaining data pipelines or ETL workflows
- Good system design and architecture skills
- Experience working with large distributed systems
- Experience of working with Kafka pipes
- Experience of working with high volume, high throughput, scalable data pipelines
- Experience working with big data pipelines and stores
- An understanding of continuous integration principles and writing testable code
- Experience using Linux/Unix
We'd love to see:
- Experience integrating AI or machine learning into data pipelines or developer tooling
- A track record of leveraging AI to improve personal or team productivity
- Familiarity with event-driven architectures and message-based data processing
- Experience with data modelling or schema design
- Comfort working with diverse groups of stakeholders, both technical and non-technical
- A desire to get involved in department and company-wide initiatives