At M&G, our purpose is to give everyone real confidence to put their money to work. With a heritage dating back more than 175 years, we have a long history of innovation in savings and investments, combining asset management and insurance expertise to offer a wide range of solutions.

Our two distinct operating segments, Asset Management and Life, work together to provide access to balanced, long-term investment and savings solutions.

Through telling it like it is, owning it now and moving it forward together with care and integrity; we are creating an exceptional place to work for exceptional talent.

We will consider flexible working arrangements for any of our roles and offer workplace adjustments to ensure you have the support you need to succeed in your role.

The Opportunity:

Data and AI are at the heart of how we plan to grow our Asset Management business. We are modernising how data is sourced, engineered and consumed across the firm. The goal is trusted, reusable data products that support investment decisions, client reporting, regulatory submissions and a growing range of AI use cases.

As Senior Data Product Manager, you will report to the Head of Data Products and own the roadmap, requirements and priorities for a portfolio of data products. You will be the main link between the business and our data teams. You will turn business needs across investment, client, operations and regulatory areas into clear, well-prioritised data products that our engineering, analytics and AI teams can deliver.

This is a business-facing product role, not a project management or reporting role. We want someone who understands investment data, can challenge vague requirements, and makes clear prioritisation decisions based on business value. Success is measured by business outcomes and adoption, not just by what gets delivered.

What You will Do:

Engage the business and shape demand

• Build strong, trusted relationships with business stakeholders, including investment teams, client and distribution, operations, risk and regulatory reporting.

• Understand business processes, pain points and opportunities, and identify where data products can deliver the most value.

• Lead demand intake for your domain, challenging requests to find the real business problem and use case before work is committed.

• Act as the voice of the data consumer, making sure products are designed around how people actually use data.

Own requirements and data product definition

• Translate business needs into clear, outcome-driven requirements, user stories and acceptance criteria that data engineering teams can deliver.

• Define each data product, including its purpose, consumers, business definitions, critical data elements, source systems and service levels.

• Work with data engineering and analysis teams to define data contracts, data quality rules and lineage, so products are trusted and auditable from the start.

• Identify data needs and dependencies early in change initiatives, so data is designed in rather than discovered midway through delivery.

Prioritise the roadmap and backlog

• Own the roadmap and backlog for your data product portfolio, aligned to the data strategy and agreed with business owners.

• Prioritise features using clear criteria such as business value, regulatory need, risk, effort and dependencies, and explain the trade-offs openly.

• Balance new change, BAU enhancements and technical debt within ring-fenced capacity, and report clearly on progress and resource use.

• Make and communicate scope decisions, saying no or not yet when needed, and prevent duplicated or one-off solutions.

Drive adoption and measure value

• Define success measures for each data product, such as adoption, data quality, timeliness, reuse and business benefit, and track them after release.

• Lead acceptance, launch and adoption, making sure products are tested, documented, published in the data marketplace and ready to use.

• Gather consumer feedback and use it to improve products, retire low-value outputs and consolidate overlapping data sets.

Enable AI-ready data products

• Work with Analytics and AI teams to make data products AI-ready, with clear business definitions and semantic models that support analytics, GenAI and agentic use cases.

• Use AI-assisted tools to speed up requirements analysis, data discovery and documentation, with appropriate human oversight.

What You will Bring:

• Significant experience in data product management, product ownership or senior business data analysis, within Asset Management or Financial Services.

• Strong knowledge of investment data, such as instruments, portfolios, positions, transactions, performance, client and reference data, across public or private markets.

• A track record of owning a data product roadmap and backlog, and making clear, value-based prioritisation decisions with senior stakeholders.

• Excellent requirements analysis skills, with the ability to turn ambiguous business needs into clear requirements, data definitions and acceptance criteria.

• Confidence engaging and influencing senior business stakeholders, including challenging requirements constructively and managing competing priorities.

• Good understanding of data management and governance, including data quality, lineage, data contracts, cataloguing and critical data elements.

• Enough technical understanding of modern data platforms, such as Snowflake, data modelling and ELT, to work credibly with engineers and judge feasibility.

• A collaborative approach, working as one team with Data Engineering, Analytics, AI, Delivery, Testing and offshore colleagues to manage dependencies and agree priorities.

• Experience working in a data product, data mesh or hub-and-spoke data operating model.

• Awareness of regulatory and reporting requirements relevant to asset management, such as client reporting, SFDR and other regulatory submissions.

Skills and Tools:

• Product discovery, roadmap and backlog management

• Requirements analysis, user stories and acceptance criteria

• Business process mapping and data mapping

• SQL for data exploration and validation

• Agile delivery tools, such as Jira and Confluence

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