Head of Data Engineering

Prudential UK Services; Greater London; Hybride; GBP 120 000 - 180 000; Plein temps

Avantages offerts par ce poste

Flexible working arrangements

Résumé du poste

M&G is seeking a Head of Data Engineering to lead a growing team across London and India, delivering a modern, governed data platform built around data products. You will own ingestion, transformation and provisioning, architecting scalable pipelines with Snowflake, ELT patterns and DataOps.

This role emphasizes practical data platform leadership with AI-enabled data products. The ideal candidate has hands-on Snowflake experience in Asset Management or Financial Services, and a track record of

Qualifications

• Significant experience in data engineering, including leading data teams in Asset Management or Financial Services.
• Hands-on experience leading Snowflake platform implementation, ideally in an asset management setting.
• Deep knowledge of modern data platform architecture, medallion and lakehouse, ELT and data modelling.
• Track record delivering data products, including semantic layer implementation for AI and analytics.
• Practical experience bringing AI into data engineering with governance and security.

Responsabilités

• Shape a modern data platform and own ingestion, transformation and provisioning on our Snowflake-centric platform.
• Implement a medallion architecture with clear standards for data promotion from raw to curated to consumption-ready layers.
• Set data engineering standards for ELT, orchestration, data modelling, testing, DataOps and CI/CD for data pipelines and observability.
• Build reusable ingestion, transformation and consumption patterns for BI, APIs, data sharing or AI.
• Lead, mentor and grow data engineers across the UK and India, shifting focus to reusable data products.

Connaissances

Data engineering leadership

Snowflake platform

Data architecture

AI integration

Outils

Snowflake

CI/CD

Description du poste

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:

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 :

• 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 core part of delivery.

What You will Bring :

• 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 embe