The Enterprise Product Platform (EPP) Data Strategy Group is focused on the end-to-end lifecycle of financial data. By bringing together our Data Acquisition, Data Platform, and API Platform initiatives, we build high-performance, scalable solutions that power Bloomberg’s most critical enterprise products. Our mission is to enhance transparency, efficiency, and integration capabilities for Bloomberg’s enterprise clients, supporting both buy-side and sell-side firms across a wide range of financial products and trading workflows.

Our team supports mission-critical workflows across multiple business areas while continuously expanding and generalizing our platforms to address new use cases. We are a collaborative, close-knit engineering organization seeking talented developers who are passionate about building scalable, resilient systems and shaping the next generation of our architecture.

Our organization is built around three core pillars:

• Data Acquisition Platform: A robust platform for acquiring, validating, and delivering high-quality financial and analytics data. Integrated across Bloomberg systems, it enables clients to seamlessly incorporate data into their workflows through reliable, adaptable, and efficient services. We are currently building the next generation of this platform to further expand its capabilities and scale.

• Data Platform: A highly scalable, low-latency data layer that manages hundreds of terabytes of instrument analytics and reference data. Our platform processes trillions of read and write operations daily across hundreds of machines, leveraging high-performance C++ services and distributed technologies. By providing reliable access to massive datasets, we enable product teams to build sophisticated features, support accurate reporting, and help clients make data-driven decisions.

• API Platform: A scalable foundation for secure, high-performance APIs that power critical financial services. We provide the tools, frameworks, and governance that enable teams to build innovative products with consistency and reliability. A key component is our high-throughput data ingestion platform, which supports large-scale portfolio, transaction, pricing, and custom-data uploads through highly parallelized batch-processing pipelines.

We’ll trust you to:

• Design and build high-performance, scalable, and fault-tolerant systems that support real-time data processing and large-scale financial workflows.

• Solve complex data engineering challenges, including frameworks for storing, retrieving, and analyzing vast datasets across Bloomberg systems.

• Own the full software development lifecycle, from technical design through deployment, while applying best practices in testing, automation, and performance tuning.

• Collaborate across teams to influence system architecture and optimize throughput across Data Acquisition, API, and Storage platforms.

• Evaluate and apply technologies in distributed computing, event-driven architecture, and real-time analytics.

You’ll need to have:

• 4+ years of experience with an object-oriented programming language such as C++, Python, or Java.

• A bachelor’s degree in Computer Science, Engineering, Mathematics, or equivalent experience.

• Strong knowledge of data structures, algorithms, and system design principles.

• Experience designing or maintaining high-throughput distributed systems or APIs.

• Strong collaboration skills and the ability to thrive in a fast-paced, agile environment.

We’d love to see:

• Experience with large-scale storage and caching technologies such as Comdb2, Redis, Cassandra, or Apache Iceberg.

• Familiarity with messaging queues and streaming frameworks such as Kafka, RabbitMQ, or Apache Flink.

• Experience in financial technology, including trading systems, order/execution management, or real-time analytics.

• Contributions to large-scale distributed computing platforms or data lakehouse architectures such as Spark, Trino, or Parquet.

• A passion for solving complex technical problems and building systems that empower other engineers.