Senior Business Data Analyst - M&G plc.

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 Role:

Senior Business Data Analysts within the Data Product function are responsible for understanding business requirements and identifying the data requirements needed to deliver a business outcome, shaping and enabling the use of high-quality data products and solutions that deliver measurable business outcomes and align to the Asset Management target data strategy. They work closely with business stakeholders, data product owners, technology delivery teams and other data governance partners to understand demand, define data scope, assess value, and translate business needs into scalable, reusable and governed data capabilities.

The role combines strong analytical capability with a product mindset: understanding user needs, challenging requirements, identifying opportunities to simplify or standardise, and helping to shape the data product roadmap. Senior Analysts play a key role in ensuring that data products are designed with clear purpose, quality expectations, ownership, usability and long-term strategic fit.

They use a range of analysis, discovery and stakeholder engagement techniques to support requirement prioritisation, design and delivery. They also guide and mentor less experienced analysts, helping the wider team adopt consistent approaches to data product discovery, design and documentation.

Key Accountabilities & Responsibilities:

Manager/Expert

• Work with business stakeholders, Data Product Owners, delivery teams and change team to understand business demand, lead or support discovery activity, and assess how needs should be met through existing, enhanced or new data products and solutions.
• Contribute to the design of data products and wider solutions aligned to the Asset Management target data strategy and the long-term data platform direction, including the shift to a governed, Snowflake-centric technology architecture with self-service semantic layers and data marketplace capabilities.
• Shape demand by challenging requirements, identifying common needs across stakeholder groups, and ensure solutions are designed for reuse, scalability and strategic fit, championing adoption to reduce bespoke, point-to-point solutions and accelerate time-to-value.
• Help define the purpose, scope and intended outcomes of data products, ensuring each has a clear business problem, defined users and measurable value. Develop well-structured product artefacts, including problem statements, data requirements, acceptance criteria, lineage and quality expectations, and operating model impacts.
• Support the creation and refinement of the data product roadmap, assessing business value, urgency, delivery complexity, governance and regulatory considerations.
• Assess data risks, quality gaps, ownership issues and business impacts across the full data lifecycle from definition and sourcing through to transformation, consumption and reporting ensuring these are factored into product design and prioritisation.
• Build and maintain strong working relationships across business functions, Investment Data Services, technology delivery teams and governance stakeholders, ensuring demand is well understood, trade-offs are transparent, and product decisions are grounded in business value.
• Work closely with technology delivery teams across requirements analysis, data modelling, integration, engineering, testing and release. This includes producing and validating source-to-target data mappings and resolving data discrepancies found during build and testing.
• Guide and mentor junior analysts and support the development of reusable standards, templates, procedures and guidance that improve consistency in data product discovery, design, delivery and documentation.
• Adopt an automation and efficiency mindset, using AI-assisted tools and techniques to accelerate data discovery, requirements documentation and testing, freeing up time for higher-value analysis and stakeholder engagement.
• Contribute to and help maintain well-structured product backlogs, working within Scrum or Kanban delivery models, and support the definition of OKRs and other measurable goals to track data product progress and maturity.

• Strong data analysis and data exploration skills, with the ability to identify patterns, investigate issues and translate findings into clear business and product implications.
• Strong stakeholder engagement skills facilitating discovery conversations, managing competing perspectives, and explaining complex data concepts and trade-offs clearly to both technical and non-technical audiences.
• Ability to understand business outcomes, challenge requirements and shape demand knowing when needs should be consolidated, prioritised, deferred, redirected to existing capabilities or built as new features.
• Good understanding of data product principles, including product purpose, user needs, ownership, reusability, quality, discoverability, scalability and lifecycle management.
• Ability to assess data quality and operating model implications, with a good understanding of data governance principles including ownership, stewardship, lineage, metadata and controlled use of data.
• Familiarity with modern cloud-native data platforms and data tooling (e.g. Snowflake, Purview), and an understanding of self-service semantic layers and data marketplace concepts as enablers of scalable data products.
• Strong understanding of core Investment Data concepts, such as instruments, positions, transactions, performance, benchmarks, portfolios and related reference data.
• Broader understanding of the Asset Management business asset classes (e.g. equities, fixed income, multi-asset, private markets), fund structures, client types and the regulatory drivers that shape data requirements.Comfortable using AI-assisted tools and automation to accelerate