Technical Product Manager — Data Manufacturing Infrastructure in London

Bloomberg runs on data. In Data, we are transforming how that data is manufactured, observed, validated, and prepared for use by clients, internal systems, and AI-driven products. Our data manufacturing infrastructure supports the pipelines that move content from acquisition through classification, validation, enrichment, modeling, and publication. As those workflows become more automated and AI-enabled, we need infrastructure that is observable, measurable, resilient, and designed for continuous improvement.

Data Management & Operations (DMO) is looking for a Technical Product Manager to help shape the next generation of data manufacturing infrastructure. This role will partner closely with DMO, partner Engineering Infrastructure, AI, and domain teams to define a product roadmap for infrastructure capabilities that support automation, observability, process analysis, semantic data readiness, and scalable production workflows.

This is not a traditional project management role. You will apply product discipline to infrastructure: translating complex methodological, operational, and Engineering needs into a clear and articulate roadmap; helping teams make explicit tradeoffs; and ensuring that infrastructure design decisions support the long‑term strategy for data manufacturing optimization and automation.

We’ll trust you to:

• Define and maintain the product roadmap for data manufacturing infrastructure in partnership with DMO and Engineering leadership, ensuring priorities are clear, defensible, and aligned to Data’s goals and strategy.
• Prioritise needs across multiple stakeholders to construct a coherent backlog that reduces complexity and achieves focus.
• Balance competing infrastructure needs, including observability, pipeline analysis, and technical migrations.
• Possess a robust knowledge of data manufacturing approaches across Data, and develop strategies that improve adoption while respecting Engineering architecture and operational constraints.
• Evaluate where agentic and LLM-based approaches add value in the data manufacturing pipeline, and where deterministic microservices, rules engines, APIs, or other traditional implementations remain the better solution.
• Partner with Engineering on new pipeline components to ensure added intelligence does not reduce observability, diagnosability, maintainability, or operational resilience.
• Maintain a clear view of technological trends and evaluate open source or third party software that may support the data manufacturing process.
• Help ensure the observability platform evolves beyond technical event monitoring into an operational intelligence layer that supports analysis, experimentation, simulation, and continuous improvement.
• Develop a structured interface between Engineering and internal stakeholders, structuring conversations to be well‑scoped, technically grounded, and actionable.
• Shape inbound demand to Engineering, helping stakeholders articulate needs in a way that is complete, prioritised, and consistent with the platform direction.
• Communicate the Engineering roadmap and platform capabilities to DMO, AI, and domain teams so they can plan their own work with greater confidence.
• Drive incremental, reversible delivery. You will help define maintainability criteria, release gates, and post-incident learning loops so that edge cases and failures are fed back into product requirements.

You’ll need to have:

• 8+ years of experience, including substantial experience in technical product management for infrastructure, platform, data pipeline, or production‑scale systems.
• Experience building product management practice in environments where it did not previously exist, including earning credibility with senior engineers before exercising influence.
• Technical fluency across microservices architecture, distributed systems, APIs, data pipelines, and platform design.
• Experience translating ambiguous business, operational, or analytical needs into clear product requirements and Engineering‑ready specifications.
• Experience defining observability, telemetry, or operational intelligence requirements as part of product design, not only as post‑deployment monitoring.
• Strong judgment about when to use AI, LLM, or agentic approaches and when simpler deterministic designs are more appropriate.
• Strong written communication skills, including the ability to produce clear product requirements, decision memos, roadmap narratives, and senior leadership updates.
• Proven ability to lead through influence across cross‑functional or matrixed teams where formal authority is limited or absent.
• A track record of building trust with technical teams through partnership, clarity, and disciplined prioritisation.

We’d love to see:

• Experience with data platforms, ETL/ELT systems, data contracts, schema governance, data quality tooling, metadata management, or lineage platforms.
• Familiarity with process analytics, statistical process control, workflow simulation, experimentation, or other methods used to evaluate operational systems.
• Experience defining infrastructure or data product requirements for AI and LLM consumption, including structured and unstructured content workflows.
• Exposure to data observability tools, lineage systems, or operational monitoring platforms, including a point of view on where these tools succeed and where they fall short.
• Experience working with semantic models, knowledge graphs, entity resolution, metadata governance, or AI‑ready data initiatives.
• Academic or professional background in computer science, data engineering, statistics, economics, operations research, or a related technical discipline.

You’ll be successful in this role if you:

• Improve the velocity and variety of content that is ingested by Data and converted into robust data products.
• Improve Data’s ability to adopt relevant, emerging technologies, as well as pivot to new or differently structured data products.
• Build credibility with engineering by demonstrating technical depth, judgment, and respect for architectural ownership.
• Help DMO, Engineering, AI, and domain teams converge on a shared roadmap for data manufacturing infrastructure.
• Turn observability and instrumentation from a monitoring function into a product capability that supports better decisions.
• Make infrastructure priorities more visible, adoption paths clearer, and tradeoffs easier for senior stakeholders to understand.
• Improve the organisation’s ability to evaluate automation opportunities empirically rather than relying on intuition, one‑off analyses, or disconnected tooling.