Software Engineering Team Leader-Data License Analytics

Bloomberg delivers billions of data points to hundreds of thousands of customers daily. When markets move, our customers need their data immediately and without fail. That's not a nice-to-have. It's the whole product.

The Data License Reliability & Analytics group exists to make sure that promise holds — and to make sure we keep it efficiently. Our new Pune-based DL Optimization & Automation team will build the AI tooling and the context layer that lets engineers, SREs, and AI agents across Data License reason about our systems the way our most experienced people do: which pipelines feed which products, where capacity is wasted, which risks are real, and what to fix first. In this role, you'll build and lead a team that puts AI to work on one of the most demanding data platforms in financial services. Your team will create intelligent systems that make Data License leaner, safer, and easier to run: Smarter use of infrastructure — finding where compute, storage, and other system resources are over-provisioned or idle, and turning that insight into action. Sharper data products — analyzing how the datasets in our Data License packages are built, delivered, and used, so we can keep improving what we offer clients. Security, risk, and compliance on autopilot — automating the evidence-gathering, tracking, and reporting that keep us ahead of evolving regulatory and security requirements. Maintenance that runs itself — finding the routine upkeep that consumes engineering time and handing it to machines. Underpinning all of this is an AI context layer you'll help design: a living, machine-readable understanding of our systems, data, and dependencies that lets engineers and AI agents answer hard questions in minutes instead of weeks. You will lead a team of four engineers, setting its technical direction and driving projects from idea to delivery. You'll work closely with engineering teams across Data License and Bloomberg infrastructure — as well as our reliability teams in New York and Dublin — turning their hardest operational questions into tools they rely on every day.