Technical Product Manager — Data Manufacturing Infrastructure

Bloomberg

Job Location:

London - UK

Monthly Salary: Not Disclosed

Posted on: 9 hours ago

Vacancies: 1 Vacancy

Job Summary

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 you

Apply 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.

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Required Experience:

Technical Product Manager Data Manufacturing Infrastructure Location ...