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M&G

Publicly traded savings and investments company

Head of Data Engineering

Engineering LeadershipConfirmed live in the last 24 hours
Full-timeLondon, UKNo salary listedIn-person

About the Job

Requirements

  • 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.
  • 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 an advantage.
  • Experience leading complex data platform migrations and the controlled retirement of legacy platforms while maintaining service continuity.
  • Experience collaborating and building effective relationships with colleagues across data and the wider technology function, sharing ownership, managing dependencies and agreeing joint priorities.
  • 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.

Responsibilities

  • Own the design, build and optimisation of ingestion, transformation and data provisioning on the 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 to speed up access to new data for the business through BI, APIs, data sharing or AI.
  • Balance data architecture with early business value using a pragmatic, outcome-led approach.
  • Make data cataloguing, data quality, lineage and data observability core parts of how every data product is built and run.
  • 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.
  • Lead, inspire and mentor data engineers across the UK and India, and set the standard for delivery.
  • Develop the team through regular coaching, clear career pathways and 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.
  • Partner with Data Product, Analytics and AI teams to implement semantic models, ensuring business definitions and relationships are consistently represented in engineered data products.
  • Apply AI across data engineering, including data quality monitoring, anomaly detection, automated documentation and AI-assisted code, with appropriate human oversight.
  • 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 optimising compute and storage costs.

Desired Qualifications

  • Databricks exposure.
  • Deeper asset management experience.

About the Company

M&G

Publicly traded savings and investments company

M&G is a savings and investments company that manages money for individuals and institutions. It offers a range of investment products, including mutual funds, where many investors pool their money to buy a fixed mix of assets chosen by the fund manager. The funds are then managed to try to grow wealth and provide long-term returns, with investors’ money divided into shares or units based on the fund’s rules. M&G differentiates itself through its long history as an asset manager, its status as an independently listed company after the demerger from Prudential, and its focus on serving the UK and European markets with a broad lineup of funds and investment strategies. Its goal is to help people save and invest for the long term by providing access to professionally managed portfolios and investment options that suit different risk tolerances and goals.

Company size
1,001-5,000
Company stage
IPO
Headquarters
London, United Kingdom
Founded
1931

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