Technical Product Manager — Data Manufacturing Infrastructure

Technical Product Manager Data Manufacturing Infrastructure Location London Business Area Data Ref #

Description & Requirements

Bloomberg runs on 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.
Well 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 Datas goals and strategy.
• Prioritize 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 prioritized 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.

Youll need to have:*Please note we use years of experience as a guide but we certainly will consider applications from all candidates who are able to demonstrate the skills necessary for the role.

• 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 prioritization.

Wed 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.

Youll 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 the Datas 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 organizations ability to evaluate automation opportunities empirically rather than relying on intuition one-off analyses or disconnected tooling.

Does this sound like youApply if you think were a good match! Well get in touch to let you know what the next steps are.
If indicated please note that years of experience are a guide; we will consider applications from all candidates who can demonstrate the skills necessary for the role. Discover what makes Bloomberg unique - watch our podcast series for an inside look at our culture values and the people behind our success.

Required Experience:

IC

Employment Type : Full Time
Experience: years
Vacancy: 1