Bloomberg

The DataHub Engineering team is building a distributed platform to host, catalog, discover, and deliver financial datasets across Bloomberg. This platform powers batch analytics, real-time stream processing, and low-latency, high-availability data distribution - ensuring that high-quality data, the lifeblood of financial markets, is always accessible.

You will join the team that introduced the abstraction of “dataset”, invented a schema language to formally define all data at Bloomberg, complete with schema evolution, versioning, and a true point in time semantics. We're the first to introduce Kafka, Avro, company-wide Dataset Schema Registry, Mesos, Clustered MySQL, Vitess and Spark for ETL at Bloomberg. We are designing a new Data Intensive Platform that is the hub of financial datasets. 

You’ll get to:

• Write software for Kafka based Data Pipes for the company wide Data Mesh

• Debug and diagnose intricate issues, functional and performance regressions, with Apache Kafka, Apache Spark, data codecs, low latency services, and streaming

• Collaborate and share extensively with fellow engineers 

• Contribute to open source technologies like Spark or Iceberg

• Display expertise in building Lakehouse for large scale data platforms

Our tech stack:

• Languages: Java, Python, Scala

• Frameworks/Tools: Spark, Kafka, Kubernetes

• Cloud-Native Stack: Container orchestration, service mesh, distributed tracing

You’ll need to have:

• 4+ years of professional experience programming in Java, Scala, or Python

• Expertise in Apache Kafka, Spark, Redis and Distributed Systems

• Experience building and testing scalable and reliable data infrastructure

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

We’d love to see: 

• Any of your contributions in open source to Kafka, Spark, Streaming, etc.

• Experience with performance optimization techniques in Iceberg and using Redis for caching expensive query results to improve application performance

• Experience with DuckDB for analytics on smaller datasets on Kubernetes

• Production experience with Kubernetes (Helm, Operators, CRDs)

• Familiarity with Kafka, Spark, or lakehouse architectures

• A passion for reliability, scale, and mentoring others