Head of Data Engineering

Eames Consulting Group; Greater London; Hybride; GBP 150 000 - 190 000; Plein temps

Avantages offerts par ce poste

Generous pension

Employee share schemes

Enhanced parental leave

Private healthcare

Résumé du poste

Eames Consulting Group is partnering with a major UK financial services group to recruit a Head of Data Engineering in London. The role focuses on designing, building and governing data products across a Snowflake-centric platform, with pragmatic, business-oriented delivery and an emphasis on AI-enabled data capabilities.

You will lead a hybrid team in London and offshore, setting standards for DataOps, CI/CD and data governance while driving scalable, policy-driven data platforms that support

Qualifications

• Significant data engineering experience, including leading data engineering teams in financial services.
• Hands-on Snowflake platform implementation experience, preferably in asset management.
• Deep knowledge of modern data platform architecture (medallion, lakehouse) and data product design.
• Experience delivering data products with semantic layers for AI and analytics.
• Knowledge of data governance tooling: catalogues, quality, lineage and observability.
• Understanding of cloud security, data privacy, access management and regulatory controls.
• Experience working with data mesh or hub-and-spoke operating models.

Responsabilités

• Own design, build and optimisation of ingestion, transformation and data provisioning on a Snowflake-centric platform.
• Implement medallion architecture with raw, curated and consumption-ready layers.
• Set standards for ELT, orchestration, modelling, testing and CI/CD for data pipelines.
• Build reusable ingestion and transformation patterns to accelerate business insights and AI enablement.
• Lead and mentor onshore and offshore data engineers to deliver outcomes.

Connaissances

Data architecture

Leadership

Cloud security

Data governance

Offshore team leadership

Data mesh

Stakeholder management

AI integration

Outils

Snowflake

Azure Databricks

SQL

Python

dbt

Description du poste

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.
• Develop the team through regular coaching, clear career paths and real opportunities to grow technical and leadership skills.
• Move the team’s focus from building individual pipelines to delivering reusable data products, backed by strong data modelling, data quality and lineage practices.

Build AI‑ready data and bring AI into data engineering

• Work with the Data Product, Analytics and AI teams to implement semantic models, so business definitions and relationships are represented consistently in the engineered data products.
• Use AI across data engineering, from data quality monitoring and anomaly detection to automated documentation and AI‑assisted coding, with appropriate human oversight.

Simplify the estate and retire legacy platforms

• Lead the technical migration from legacy data platforms and point‑to‑point 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.
• Make cost awareness part of data engineering by designing efficient pipelines and optimising compute and storage costs as a core part of delivery.

What you will bring

• 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 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 layers for AI and analytics.
• Practical experience building data governance tooling into delivery, covering data catalogue, data quality, lineage and data observability.
• A strong understanding of cloud security, data privacy, access management and regulatory control requirements.
• A track record of building effective working relationships across data and the wider technology function, including sharing ownership, managing dependencies and agreeing joint priorities.
• Experience leading and developing offshore engineering teams as part of one function, with shared capability, consistent standards and a one‑team culture.
• Experience working in data mesh or hub‑and‑spoke data operating models.
• A good understanding of investment data, such as instruments, portfolios, positions and performance. Deeper asset management experience is a strong advantage.
• Experience leading complex data platform migrations and retiring legacy platforms in a controlled way without disrupting service.

Technical toolkit

• Snowflake, including Snowpipe, Streams and Tasks, Dynamic Tables and Snowpark
• Azure, with Databricks exposure an advantage
• 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
• Data catalogue, data quality, lineage and data observability tooling
• AI‑assisted data engineering and data quality monitoring tools

Why apply

This is a chance to shape a data engineering practice at a key moment for the business. Data engineering is at the centre of the organisation’s data and AI plans, and you will have real influence over how the platform and the team develop. You will see the direct impact of your work on how the business invests, serves its clients and operates. If you want to lead, build and grow a team all at once, this role offers that.

The package is competitive and includes:

• a generous pension with strong employer contributions
• employee share schemes and financial wellbeing support
• above‑market annual leave, with the option to buy more
• market‑leading family‑friendly policies and enhanced parental leave
• private healthcare, critical illness cover and life assurance, with family options