The Analytics Platform department builds the distributed infrastructure that powers Bloomberg's core analytics ecosystem, including CalcrtX, BQL, and the evolution of the legacy Calcrt platform. Our systems form the foundation for how Bloomberg applications and clients access structured data, analytics, and real-time content at global scale.

We are modernizing one of Bloomberg's most critical platforms by transforming a decades-old monolithic architecture into a distributed, service-oriented ecosystem. Every day our platform processes more than 700 billion requests, providing the data infrastructure behind Bloomberg Terminal, Enterprise products, and analytics services used across the company.

Our mission is to simplify how data producers publish content and how consumers access it by building scalable compute platforms, routing and orchestration layers, standardized APIs, and reusable infrastructure. We develop the technologies that enable data providers to expose analytics through modern microservices while maintaining compatibility with Bloomberg's existing ecosystem, with a strong focus on making it easy to onboard new content and capabilities.

Engineers on our team work across a broad range of distributed systems challenges, including:

• Building high-performance gateways that route, authorize, cache, isolate traffic, enforce fairness, and meet strict service-level objectives for Bloomberg's analytics platforms.



• Designing the next generation of Calcrt, Bloomberg's distributed analytics platform, enabling seamless integration of new data providers and simplifying the onboarding of new content.


• Developing Bloomberg's company-wide Entity Resolution platform, responsible for resolving both securities and non-financial entities while providing a shared foundation for products across Bloomberg.


• Building Bloomberg's centralized Entitlement platform, providing shared authorization infrastructure used across Bloomberg products and analytics platforms.


• Creating reusable frameworks, SDKs, and developer tooling that enable engineering teams across Bloomberg to publish data and analytics using standardized interfaces.



• Improving the throughput, efficiency, and query execution model of Bloomberg's federated analytics engine while modernizing its architecture with reactive programming techniques and leveraging AI-assisted engineering tools to accelerate development.

Our engineering culture emphasizes correctness, observability, distributed systems reliability, and performance engineering. We build foundational platforms and services used by engineering teams across Bloomberg, with a strong focus on long-term maintainability and operational excellence.

Our engineering stack spans Modern C++, Python, Linux, Apache Arrow, distributed microservices, and asynchronous frameworks, with increasing adoption of AI-assisted development tools throughout our engineering workflows.

Tech Stack:

Programming Languages: Modern C++, Java, Python

Frameworks: Spring Boot, Project Reactor, Apache Kafka, Apache Arrow

Caching: Redis

Databases: Elastic Search, PostgreSQL

Containerization: Docker, Kubernetes

CI/CD: Jenkins, SonarQube

Infrastructure: Linux, Microservices, Async frameworks

What's in it for you?

If you're excited about working on large-scale distributed systems, core analytics infrastructure, and platforms that serve thousands of engineers and clients daily, you'll have a home here.

Analytics Platform is at the heart of Bloomberg's data and analytics ecosystem. Whether it's scaling BQL, evolving CalcrtX, or building the next generation of shared analytics services, our work touches nearly every Bloomberg product and client experience. You'll have the chance to:

• Re-architect the next generation of our infrastructure.



• Design, implement and own critical services and platform components.


• Build the analytics foundation that will power Bloomberg for the next decade.


• Work on high-visibility projects with many opportunities to interact with Senior Management.



• Incorporate open-source and industry-standard solutions to solve challenging engineering problems.

  
  

We'll trust you to:

• Shape the architecture of next-generation systems and design scalable, resilient microservices and APIs from the ground up.



• Solve distributed systems problems at multi-billion-request-per-day scale, optimizing latency, throughput, and resource usage.


• Collaborate with dozens of teams across Engineering—including data provider and client teams—to redefine how data flows across Bloomberg.


• Build tooling, frameworks, and AI-driven automation that improve developer experience and simplify operating large-scale distributed systems.


• Ensure operational excellence, reliability, and observability across systems using a test-driven approach and high-quality code.


• Take ownership of your projects, define "done," and make it so.



• Champion good engineering practices and mentor others.

  
  

You'll need to have:

• 4+ years of experience, ideally in Modern C++, Python and/or Java.



• Strong system design and distributed systems fundamentals.


• Deep understanding of object-oriented design, data structures, and algorithms.


• Familiarity building and testing scalable and reliable data infrastructure.


• Comfort with high-performance, high-volume, high-availability distributed systems.


• A collaborative mindset with excellent communication skills.



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

  
  

We'd love to see:

• Experience with frameworks such as Apache Arrow, Spring Boot, Project Reactor, or Apache Kafka.



• Hands-on experience building non-blocking, reactive, or asynchronous applications in a service-oriented architecture.


• Experience with profiling, benchmarking, and performance optimization, including low-latency system design and compute/memory trade-offs.


• Knowledge of Linux system programming or performance engineering.


• Comfort with databases (Elastic Search, Comdb2, PostgreSQL) and caching systems (Redis).


• Experience with cloud-native technologies (Docker, Kubernetes).


• Hands-on experience applying AI/ML techniques (e.g., NLP, predictive modeling) to engineering challenges, along with an interest in incorporating emerging technologies into production systems.


• Contributions to large-scale infrastructure or open-source projects.



• Interest in mentorship, technical leadership, and cross-team collaboration.