Senior Software Engineer - Data Technologies Pipelines

Location

London

Business Area

Engineering and CTO

Ref #

Description & Requirements

Who are we?

Bloomberg onboards vast volumes of external data that ultimately power products such as the Bloomberg Terminal. Our team builds infrastructure that enables engineering teams and data management professionals to ingest, process, and operate those data flows reliably at scale.

DTP (Data Technologies Pipelines) is our Kubernetes-based platform for building and operating event-driven microservices at scale. Engineering teams and data management professionals use DTP to build data ingestion pipelines from small services that process billions of events per day. Built-in features such as traffic mirroring and observability help teams safely test changes in production-like environments and understand how their systems behave as incoming data changes.

We build systems that scale horizontally and are designed for reliability, usability, and operational simplicity. They are primarily written in Python and Go, built on top of Kubernetes, and leverage technologies such as Kafka, S3, Comdb2, and OpenTelemetry.

What’s in it for you?

You will work on a large-scale platform with wide impact across Bloomberg Engineering and Data. The role combines distributed systems, developer experience, production operations, and product-minded platform engineering.

You will have the opportunity to:

• Build features that scale Bloomberg’s data acquisition business.
• Own feature development start-to-finish, from design through rollout.
• Improve the reliability, scalability, and usability of systems processing billions of events per day.

Who are you?

You care about building reliable production systems that are intuitive for users and drive the business forward.

You are autonomous without being isolated: you can take a problem, explore the trade-offs, make a plan, communicate it clearly, and drive the work forward with your team.

You are comfortable with distributed systems, production debugging, and operational trade-offs. You expect failures to happen, and you think about redundancy, recovery, and graceful degradation when designing systems.

You are technically curious, pro-active, open to feedback, and comfortable changing your approach when the evidence changes.

We’ll trust you to:

• Design, build, test, and operate infrastructure and platform features for event-driven data pipelines.
• Work across the stack to build simple and reliable user-facing abstractions over complex distributed infrastructure.
• Own our systems end-to-end, from identifying problems through to production rollout and on-call support.
• Identify technical and feature gaps, write clear proposals, communicate trade-offs, and collaborate with teams across Bloomberg to deliver improvements.
• Contribute to operational excellence through testing, monitoring, troubleshooting, incident response, and post-incident learning.

You’ll need to have:

• 4+ years of backend engineering experience in Python, Go, or a comparable language, and occasional contributions to frontend development.
• A degree in Computer Science, Engineering, Mathematics, similar field of study or equivalent work experience.
• Hands-on experience building and maintaining distributed systems in production.
• Knowledge of event-driven architectures using Kafka or similar technologies.
• Good understanding of Linux systems, networking fundamentals, and production debugging.
• A product mindset: you care about user impact, developer experience, and making systems easier to use.
• Clear written and verbal communication, including design documents, operational updates, and incident follow-ups.

We’d love to see:

• Experience building platforms, frameworks, or infrastructure used by other software engineers.
• Experience running systems on Kubernetes or other containerised runtimes.
• Experience with telemetry technologies such as Prometheus, Grafana, OpenTelemetry, or similar.
• Experience with scalable storage technologies such as S3, Cassandra, or comparable systems.
• Experience with deployment automation and reproducible operations.

If indicated, please note that years of experience are a guide; we will consider applications from all candidates who can demonstrate the skills necessary for the role.

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