Head of Data Engineering, M&G
Salary not provided
+ Access to our Share Save and Share Incentive Plan
SQL
Python
Azure
Snowflake
dbt
Senior and Expert level
London
International savings and investments company
Posted today
International savings and investments company
1001+ employees
B2CB2BEnterpriseInvestingFinancial Services
Posted today
Salary not provided
+ Access to our Share Save and Share Incentive Plan
SQL
Python
Azure
Snowflake
dbt
Senior and Expert level
London
1001+ employees
B2CB2BEnterpriseInvestingFinancial Services
Company mission
To help people manage and grow their savings and investments, responsibly.
Role
Who you are
- Significant experience in data engineering, including experience leading data engineering teams within Asset Management or Financial Services
- Hands-on experience leading Snowflake platform implementation, ideally in an asset management setting
- Deep technical knowledge of modern data platform architecture, including medallion and lakehouse patterns, ELT, and dimensional or domain data modelling
- A track record of designing and delivering data products, including semantic layer implementation for AI and analytics use
- Practical experience embedding data governance tooling into delivery, covering data catalogue, data quality, lineage, and data observability
- Strong understanding of cloud security, data privacy, access management, and regulatory control requirements
- Track record of collaborating and building effective relationships with colleagues across data and the wider technology function. You should be able to share ownership, manage dependencies, and agree joint priorities
- Experience leading and developing offshore engineering teams as an integrated part of the function, building capability, consistent standards, and a one-team culture
- Experience working in data mesh or hub-and-spoke data operating models
- Good understanding of investment data, such as instruments, portfolios, positions, and performance; deeper asset management experience is a strong advantage
- Experience leading complex data platform migrations and the controlled retirement of legacy platforms while maintaining service continuity
- Experience with Snowflake, including Snowpipe, Streams and Tasks, Dynamic Tables, and Snowpark
- Experience with Azure; Databricks exposure is an advantage
- Advanced SQL and Python
- Experience with data transformation frameworks such as dbt
- Experience with data pipeline orchestration and DataOps / CI/CD for data
- Experience with streaming, CDC, and event-driven integration
- Experience with API, data sharing, and consumption patterns
- Experience with performance engineering and FinOps
- Experience with data catalogue, data quality, lineage, and data observability tooling
- Experience with AI-assisted data engineering and data quality monitoring tools
What the job involves
- 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 Head of Data Engineering, you will report to the Director of Data, AI & Analytics and lead a growing data engineering team across London and India. You will own how we ingest, transform, and deliver data. You will move us from fragmented, pipeline-by-pipeline delivery to a modern, governed architecture built around data products. This is a data engineering leadership role, not a general engineering management role. We want a practitioner who has built modern data platforms hands-on and knows how to use modern data technologies to design scalable solutions that deliver real business outcomes
- Shape a modern data platform: own the design, build, and optimisation of ingestion, transformation, and data provisioning on our Snowflake-centric platform
- Implement a medallion architecture, with clear standards for promoting data from raw to curated to consumption-ready layers
- Set data engineering standards for ELT patterns, orchestration, data modelling, testing, DataOps, CI/CD for data pipelines, and observability
- Build reusable ingestion, transformation, and consumption patterns that speed up access to new data for the business, whether through BI, APIs, data sharing, or AI
- Take a pragmatic, outcome-led approach, balancing the right data architecture with early business value
- Deliver governed, trusted data products: make data cataloguing, data quality, lineage, and data observability core parts of how every data product is built and run, rather than controls added after delivery
- Include data contracts, metadata, and certification standards in every data product, so each one is discoverable, trusted, and auditable
- Ensure data products and pipelines are secure and compliant by design, embedding identity and role-based access control, secrets management, encryption, data classification, retention, and privacy controls into engineering standards
- Build and lead a high-performing data engineering team: lead, inspire, and mentor data engineers across the UK and India, and set the standard for how we deliver
- Develop the team through regular coaching, clear career pathways, and meaningful opportunities to build technical and leadership capability
- Shift the team's focus from building individual pipelines to delivering reusable data products, underpinned by strong data modelling, data quality, and lineage practices
- Build AI-ready data and bring AI into data engineering: partner with Data Product, Analytics, and AI teams to implement semantic models, ensuring business definitions and relationships are consistently represented in the engineered data products
- Apply AI across data engineering, from data quality monitoring and anomaly detection to automated documentation and AI-assisted code, with appropriate human oversight
- Simplify the estate and retire legacy platforms: lead the technical migration from legacy data platforms and point-to-point data feeds onto the strategic platform
- Consolidate duplicated pipelines and fragmented data stores into a smaller set of trusted, reusable data products
- Reduce technical debt and keep the data estate simple, supportable, and cost-efficient
- Build cost awareness into data engineering by designing efficient pipelines and data products and by optimising compute and storage costs as a core part of delivery
- This is a chance to shape a data engineering practice at a pivotal moment for the business. Data engineering sits at the centre of our data and AI ambitions, and you will have real influence over how our data platform and data engineering capability develop
- You will see the direct impact of your work on how we invest, serve our clients, and operate. If you want to lead, build, and grow a team at the same time, this is the role
Application process
- End Date: November 28, 2026
Salary benchmarks
Our take
Established through a merger with Prudential's UK and Europe operation in 2017, M&G has emerged as a leading savings and investment company. Combining five prominent brands under one umbrella - M&G Real Estate, M&G Investments, Pru, M&G Wealth, and Infracaptial - the company boasts a robust global financial services operation.
M&G's commitment to enhancing its platform's offerings is evident through substantial investments in service and support infrastructure. Innovations such as deep back-office integration, cloud-based customer relationship management tools, and e-signature software underscore the company's dedication to providing top-notch solutions.
Catering to a diverse clientele ranging from individuals to major enterprises, M&G continues to expand its international presence. It reported an impressive £1.8 billion in flows from open business in 2025, and continues to diversify and leverage advancements in AI and Big Data. This approach should help the company maintain its position as a key player in the ever-evolving landscape of savings and investment.
Steph
Company Specialist at Welcome to the Jungle
Insights
3% employee growth in 12 months
Company
Company benefits
- 8% non-contributory pension plus up to 5% matching
- Life Assurance & Death in Service
- Support, feedback and development programmes to help you reach your full potential
- Flexible culture and ways of working
- Focus on health & wellbeing
- 30 days holiday as standard (excluding bank holidays), with the ability to purchase an additional 5 days
- Time off when you need it includes carer's leave, faith leave, grandparent leave and career breaks
- New parents receive up to 26 weeks of full pay while on leave
- 4-week back-to-work transition for new parents: work 80% of your contractual hours, be paid for 100%
- New parents can take family leave in flexible blocks within the first year of birth/adaoption
Company HQ
City of London, London, UK
Leadership
Andrea Rossi
(Group CEO)Co-Founder & Director at REsustain. Former CEO of AXA, and was an FC Girondins de Bordeaux Board Member.
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