Head of Data Engineering
Head of Data Engineering at M&G's asset management business, leading a UK-and-India team to build a governed, Snowflake-centric data product platform — owning ELT, medallion architecture, data quality/lineage, legacy migrations and AI-ready data delivery. Core stack: Snowflake, Azure, SQL, Python, dbt, streaming/CDC and DataOps/CI-CD.
- Compensation
- Not disclosed
- Location
- London, United Kingdom
- Work setup
- Not specified
- Seniority
- C level
- Employment
- Not specified
- Experience
- Not specified
- Category
- Data engineering
- Posted
Skills
Must have
- Significant data engineering experience, including leading data engineering teams in Asset Management or Financial Services
- Hands-on experience leading Snowflake platform implementation, ideally in asset management
- Deep knowledge of modern data platform architecture: medallion/lakehouse, ELT, dimensional or domain data modelling
- Track record designing and delivering data products, including semantic layer implementation for AI and analytics
- Practical experience embedding data governance tooling: data catalogue, data quality, lineage, data observability
- Strong understanding of cloud security, data privacy, access management and regulatory control requirements
- Track record of cross-functional collaboration, sharing ownership and managing dependencies
- Experience leading and developing offshore engineering teams
- Experience working in data mesh or hub-and-spoke data operating models
- Understanding of investment data (instruments, portfolios, positions, performance)
- Experience leading complex data platform migrations and controlled retirement of legacy platforms
- Snowflake toolkit: Snowpipe, Streams and Tasks, Dynamic Tables, Snowpark
- Advanced SQL and Python
- Data transformation frameworks such as dbt
- Data pipeline orchestration and DataOps / CI/CD for data
- Streaming, CDC and event-driven integration
- API, data sharing and consumption patterns
- Performance engineering and FinOps
- AI-assisted data engineering and data quality monitoring tools
Nice to have
- Deeper asset management experience
- Azure, with Databricks exposure an advantage
Full description
The 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 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 be moving 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. You should know how to use modern data technologies to design scalable solutions that deliver real business outcomes.
What You will Do:
- 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.