Head of Data Engineering

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.

• 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, Data Ops, 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.

• 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…