Head of Data Engineering
Leads the data engineering function for a UK asset management group, owning a Snowflake-centric platform, governed data products, legacy migrations and an onshore/offshore team based in London. Core stack: Snowflake, Azure, dbt, SQL and Python, with Databricks exposure an advantage.
- 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 a Snowflake platform implementation, ideally in asset management
- Deep knowledge of modern data platform architecture: medallion and lakehouse patterns, ELT, dimensional/domain data modelling
- Track record designing and delivering data products, including semantic layers for AI and analytics
- Practical experience building data governance tooling into delivery (catalogue, data quality, lineage, observability)
- Strong understanding of cloud security, data privacy, access management and regulatory control requirements
- Track record of building effective working relationships across data and the wider technology function
- Experience leading and developing offshore engineering teams as one function with shared standards
- Experience working in data mesh or hub-and-spoke data operating models
- Good understanding of investment data: instruments, portfolios, positions, performance
- Experience leading complex data platform migrations and retiring legacy platforms without disrupting service
- Snowflake, including Snowpipe, Streams and Tasks, Dynamic Tables and Snowpark
- Azure
- 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
Nice to have
- Deeper asset management experience
- Databricks exposure
Full description
Our client is a well‑established UK financial services group with a large asset management business at its core. It is investing heavily in data and AI as a key part of its growth plans. Flexible working and workplace adjustments are available for this role.
The role
Data and AI are central to how the asset management business plans to grow. The organisation is changing how data is sourced, engineered and used across the firm. The aim is trusted, reusable data products that support investment decisions, client reporting, regulatory submissions and a growing number of AI use cases.
As Head of Data Engineering, you will report to the senior leader for data and AI. You will lead a growing data engineering team based in London and an offshore delivery centre. You will own how data is ingested, transformed and delivered. Your job is to move the function away from building one pipeline at a time and towards a modern, governed architecture built around data products.
This role leads data engineering specifically. It is not a general engineering management role. The organisation wants a practitioner who has built modern data platforms hands‑on and can use modern data technologies to design scalable solutions with real business outcomes.
What you will do
- Own the design, build and optimisation of ingestion, transformation and data provisioning on a 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 so the business can get to new data faster, whether through BI, APIs, data sharing or AI.
- Be pragmatic and focused on outcomes, balancing the right data architecture against delivering business value early.
Deliver governed, trusted data products
- Build data cataloguing, data quality, lineage and observability into how every data product is designed and run, rather than adding them as controls after delivery.
- Include data contracts, metadata and certification standards in every data product so each one is discoverable, trusted and auditable.
- Make sure data products and pipelines are secure and compliant by design. This means building identity and role‑based access control, secrets management, encryption, data classification, retention and privacy controls into engineering standards.
Build and lead a high‑performing team
- Lead, inspire and mentor data engineers across onshore and offshore locations, and set the standard for how the team delivers.