Bloomberg runs on data. It’s our business and our product. It’s why thousands of companies partner with us. We're nearing one petabyte and growing, with no end in sight. Our data captures the who, what, when, where and why our clients use Bloomberg products.

The Bloomberg Data Services (BBDS) Engineering team provides a distributed platform for hosting data sets, complete with managed data stores, search, analytics, and real-time stream processing capabilities. Our system scales to petabytes while offering low latency, high availability, and discoverability of data to clients. BBDS is designed to address the unique and evolving challenges in today’s financial systems.

Do you enjoy solving intricate engineering problems? Like the challenge of building real-time, large-scale systems? Are you the type of developer who digs in at the lower levels and demands to understand how software systems work? Do you strive to write meticulous and maintainable code that accounts for all possible failures? If this sounds like you, we’re interested in speaking to you!

As an engineer in Data Technologies, you’ll be responsible for the systems that onboard all the referential data that drive Bloomberg's applications and enterprise systems. As our clients are shifting more and more to rely on machines to interpret data and drive insights, we are utilizing cutting edge technologies to deliver unparalleled data quality. By joining Data Technologies, you will help us improve the accuracy, coverage, timeliness, and accessibility of our data to service our clients across all of Bloomberg's products. Learn more about the Data Technologies teams at Princeton here: https://www.youtube.com/watch?v=qtUu9LCNmiU

You’ll need to have:

• A background in Computer Science, Engineering, or Math.
• 4+ years of professional experience in Java/Scala with deep knowledge of the JVM.- Experience designing services that scale to millions of requests a second.- Proficiency in Linux, kernel subsystems, TCP/IP.
• Knowledge of database internals (e.g. MySQL), transactions, clustering, sharding, and replication.
• Experience in software instrumentation for monitoring and observability.
• Software best practices: automated testing, continuous integration, and documentation. Experience with containers and cluster managers is a plus, e.g. Docker, Mesos, Kubernetes.
• BA, BS, MS, PhD in Computer Science, Engineering or related technology field