Technical Product Manager — Data Manufacturing Infrastructure
Location
London
Business Area
Data
Ref #
10052164
Description & Requirements
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.
- 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.
You’ll 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.
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 the 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 organization’s 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 we're a good match! We'll get in touch to let you know what the next steps are.
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