Bloomberg’s financial data and analytics are defined by their timeliness and quality. Our Anomaly Detection team is building a scalable, adaptable platform to help detect performance and data quality issues early in the development cycle across Bloomberg’s core data and analytics systems.

We thrive on collaboration and value diverse ideas. If you’re passionate about solving high-scale, high-availability challenges that drive meaningful impact, we’d love to talk to you.

The role:

We are seeking a highly skilled senior engineer to help evolve our platform into a more robust, scalable, and self-service solution. You will play a key role in designing and building infrastructure that powers various testing and anomaly detection capabilities, with a strong focus on reliability, extensibility, user experience for end users, and developer experience for teams extending the platform.

What’s in it for you:

You’ll tackle challenging problems at the intersection of platform engineering and large-scale data processing, helping to build a resilient system that identifies data anomalies and performance regressions in real time. You’ll collaborate with product owners, application developers, and infrastructure teams to deliver a seamless platform experience that enables faster testing and validation.

Your work will have a direct impact on the quality and stability of Bloomberg’s core financial data and analytics ecosystem. Beyond core engineering, you also have the opportunity to shape our broader strategy for ensuring data quality and software stability across the ecosystem.

You'll need to have:

• 4+ years of professional software development experience in Python or similar languages

• A degree in Computer Science, Engineering, Mathematics, a similar field of study or equivalent work experience

• Hands-on experience designing and building large-scale distributed systems

• Strong system design and problem-solving skills

We'd love to see:

• Experience building platforms that serve a large number of tenants

• Familiarity with large-scale data processing frameworks (e.g., Spark, Flink, Kafka)

• Passion for data quality, performance analysis, anomaly detection

• Familiarity with testing infrastructure, CI/CD workflows, or developer productivity tooling

• Experience with owning end-to-end delivery of complex engineering projects