Lead Data Engineer

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.

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 Lead Data Engineer, you will report to the Head of Data Engineering and be the technical lead for designing and building data products on our Snowflake-centric platform.

You will lead data engineers across London and India, turning our architecture and engineering standards into well-engineered, production-ready pipelines and data products. This is a hands-on technical leadership role. We want an engineer who still builds, sets the bar for engineering quality and can guide others through complex design decisions. You will be the bridge between our data engineering standards and day-to-day delivery, making sure what we build is scalable, governed and delivers real business outcomes.

• Design and build on a modern data platform
• Lead the hands-on design and build of ingestion, transformation and data provisioning pipelines on our Snowflake-centric platform.
• Apply the medallion architecture in practice, promoting data from raw to curated to consumption-ready layers in line with agreed standards.
• Translate business and data product requirements into robust technical designs, data models and delivery plans.
• Build and evolve reusable ingestion, transformation and consumption patterns, whether for BI, APIs, data sharing or AI.
• Help shape data engineering standards for ELT, orchestration, data modelling, testing, Data Ops, CI/CD and observability, working with the Head of Data Engineering.
• Engineer governed, trusted data products
• Build data quality checks, lineage, cataloguing and data observability into every pipeline and data product from the start, rather than adding them after delivery.
• Implement data contracts, metadata and certification standards so each data product is discoverable, trusted and auditable.
• Apply security and compliance by design, including role-based access control, secrets management, encryption, data classification, retention and privacy controls.
• Make sure pipelines are production-ready, with clear monitoring, alerting and runbooks, and lead root-cause analysis and resolution of data issues.
• Lead and develop data engineers
• Provide day-to-day technical leadership to data engineers across the UK and India, setting clear priorities and removing blockers.
• Lead design reviews, code reviews and pairing to raise engineering quality and consistency across the team.
• Coach and mentor engineers, helping them grow their technical skills and shift from building individual pipelines to delivering reusable data products.
• Work with offshore engineers as one integrated team, with shared standards, ways of working and ownership.
• Build AI-ready data and use AI in engineering
• Work with Data Product, Analytics and AI teams to implement semantic models, ensuring business definitions and relationships are consistently represented in the engineered data products.
• Use AI-assisted tools across the engineering lifecycle, from code generation and testing to documentation and data quality monitoring, with appropriate human oversight.
• Simplify the estate and retire legacy platforms
• Lead the technical delivery of migrations from legacy data platforms and point-to-point data feeds onto the strategic platform, with data reconciliation and controlled cut-over.
• Consolidate duplicated pipelines into reusable data products and actively reduce technical debt.
• Design efficient pipelines and optimise compute and storage so cost efficiency is built into…