Data Product Manager

Company Description

Experian is a global data and technology company, powering opportunities for people and businesses around the world. We operate across a range of markets, from financial services to healthcare, automotive, agribusiness, insurance, and many more. Experian invests in people and new advanced technologies to unlock the power of data. We have an amazing team of 25,200 people in 32 countries.

Job Description

At Experian Consumer Services, data is a core product capability. Our products depend on reliable event capture, trusted customer data, reusable data products, analytics and AI to improve customer experiences and create value through our Marketplace.

We are looking for a Data Product Manager to turn our data strategy into a clear, prioritised and measurable portfolio of data products.

This role will connect customer and commercial priorities with the domain teams, Data Platform, Engineering, Analytics, AI, Operations, Finance, Security and Governance capabilities required to deliver them. You will create clarity across priorities, ownership, dependencies, risks, adoption and value.

Working across the UK and Hyderabad, you will help us move from a central, request-driven data model towards domain-owned data products, self-service analytics and stronger data accountability.

What you will do:

Shape the data product strategy

• Translate ECS strategy, the FY27 product roadmap, Data 4.0 and AI priorities into a coherent data product strategy and roadmap.
• Prioritise data initiatives based on customer value, commercial impact, feasibility, dependencies and risk.
• Ensure that data investments support acquisition, activation, engagement, Marketplace conversion, lender matching, monetisation and platform reliability.

Establish product ownership and lifecycle discipline

• Define the minimum standards for a data product, including purpose, consumers, inputs, outputs, KPI definitions, quality expectations, freshness, privacy classification, owner, support model and retirement plan.
• Create clear ownership across domains, central teams and shared capabilities.
• Identify opportunities for reuse across domains and prevent duplicated metrics, pipelines and reporting logic.
• Support the transition to a domain-owned and self-service data operating model.

Connect strategy to execution

• Maintain one integrated view of use cases, domain adoption, source onboarding, data products, migration, dual running, consumer cutover and legacy decommissioning.
• Work with Product, Engineering and Analytics leads to sequence delivery around the highest-value use cases and dependencies.

Manage risks proactively

• Identify delivery, data quality, privacy, security, capacity, supplier and operational risks before they become incidents or delivery failures.
• Maintain clear risk and dependency views with leading indicators, mitigation actions, accountable owners and decision dates.

Measure adoption and value

• Define how data product success will be measured, including adoption, reuse, quality, freshness, time to insight, delivery cycle time and operational reliability.
• Work with Product, Analytics and Finance to connect data delivery to business outcomes such as leads, conversion, upsell, engagement, retention, revenue per user, productivity and platform cost.
• Track value delivery against the business case and recommend reprioritisation when assumptions or market conditions change. Ensure that priority use cases have evidence of consumer adoption, business impact and readiness for continued operation.

Qualifications

• Significant experience in product management, especially data products, data platforms.
• Practical experience with domain ownership, self-service data, data governance, data migrations or data operating model change, and experience working with data engineering teams.
• Experience managing a portfolio of interconnected initiatives rather than a single product backlog.
• Strong understanding of the data lifecycle, including event design, ingestion, modelling, data contracts, quality, metadata, lineage, analytics and activation.
• Enough technical fluency to challenge assumptions, understand trade-offs and connect product decisions to architecture, quality, privacy, reliability and cost.
• Strong commercial judgement and experience linking product or data investments to measurable business value.
• Proven ability to influence senior stakeholders and d…