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

. 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. What you'd actually be working on Building and leading the team. Build and grow a team of four engineers. Set a high bar for engineering quality and analytical rigor, run the team's planning and delivery, and create an environment where people ship useful things quickly and learn fast. You'll stay hands-on — this is a lead who writes code and reviews designs, not just one who runs meetings. The AI context layer. Design and evolve the knowledge foundation our AI tools stand on: architecture knowledge bases, semantic models and ontologies of our data and services, and knowledge graphs that connect code, configuration, telemetry, and incident history. You'll own retrieval quality across RAG and GraphRAG pipelines, decide how knowledge is structured, verified, and kept fresh, and make sure an agent's answer can always be traced back to a source. AI-driven optimization of systems, processes, and architecture. Turn one-off investigations into repeatable tooling. Build analytics that find underused machines and tenancies, unneeded storage and datasets, and architectural bottlenecks — and quantify the impact of acting on them. If we answered a question once by hand, you'll be figuring out how to answer it on demand. Agents and MCP tooling. Build MCP servers and AI agents that expose Data License systems and knowledge to engineers and to other agents, and use agent-to-agent (A2A) patterns to compose them into multi-step workflows. Work on context retrieval, prompt and tool design, evaluation frameworks, and the feedback loops that make these systems more accurate over time. Risk and compliance analytics. Own the automated analytics behind regulatory and risk programs such as emerging cyber-resilience regulation and enterprise risk management: scanning codebases and inventories, tracking remediation, and producing evidence that holds up to scrutiny from auditors and leadership. Tools people actually use. Build the web applications, dashboards, and reports (React, JavaScript/TypeScript, Python) that put these insights in front of engineers, managers, and leadership, and make them self-service. Shaping the AI Development Lifecycle (AI-DLC). Help define how Data License builds software with AI agents — agentic development workflows, guardrails, and evaluation. Your team will be an early adopter and an example for the rest of the organization. Partnering across Data License and Bloomberg infrastructure. Work directly with application, platform, and SRE teams to understand their problems, review designs, and bring data to architecture decisions. You'll be the person teams come to when they want to know what a change will really cost, what it will save, and what it might break. What you bring 7+ years writing production software, with strong Python and solid JavaScript/TypeScript 2+ years leading engineers — as a team lead, tech lead, or in a role where you owned a team's technical direction and delivery Degree in Computer Science, Engineering, Mathematics, or equivalent hands-on experience Hands-on experience building with LLMs: RAG pipelines, prompt and tool design, agents, or evaluation of AI output quality Experience building full-stack applications, including React front-ends and Python back-end services and APIs A data-driven mindset: you measure before you optimize, you show your evidence, and you're skeptical of conclusions — including an AI's — that can't be traced to a source Strong communication skills. You'll lead a team in Pune while partnering with teams in New York, Dublin, and elsewhere, so you need to explain trade-offs clearly and build consensus without authority Willingness to pick up new tools fast — this field changes month to month What would set you apart Experience with GraphRAG, knowledge graphs, ontologies, or semantic modeling Building or deploying MCP servers, multi-agent systems, or agent-to-agent (A2A) protocols in production Experience defining evaluation frameworks for LLM and agent systems — retrieval accuracy, groundedness, regression testing Background in SRE, observability, or infrastructure analytics: capacity planning, cost and utilization optimization, telemetry (metrics, logs, traces) Familiarity with data platforms such as Spark, Iceberg, Trino, or PostgreSQL Experience with containerization (Docker, Kubernetes) and cloud or virtualized compute environments Exposure to software supply-chain security, regulatory compliance, or risk management programs A track record of building a team from the ground up, or of hiring and growing engineers into leads Experience in a consulting or advisory role where you influenced engineering decisions across multiple teams If this sounds like you: Apply if you think we're a good match. We'll get in touch to let you know what the next steps are, but in the meantime, feel free to have a look at this: Introducing our Pune Engineering teams Bloomberg Engineering Technology at Bloomberg Learn more about our Pune office and benefits Discover what makes Bloomberg unique - watch our podcast series for an inside look at our culture, values, and the people behind our success.