Head of Data Engineering

Wolverhampton
5 months ago
Applications closed

Related Jobs

View all jobs

Head of BI Business Intelligence and Data

Head of Business Intelligence & Data Analytics

Data Engineer

Senior Data Engineer

Senior Data Engineer

ERP Data Analyst

Head of Data Engineering

Our long-standing financial services client is seeking a Head of Data Engineering to lead a high-performing team responsible for delivering a complex data transformation
.
Looking for a true greenfield challenge? This is your chance to shape a transformation from the ground up, build cutting-edge data solutions, and lead a high-performing team that’s driving change across the business.

To succeed in this role, you’ll bring hands-on experience owning and delivering Azure-based data platforms (Databricks, Synapse), alongside strong technical capability in designing and implementing data ingestion pipelines (ETL/ELT) and introducing event-driven architectures (Kafka) to support scalable, real-time solutions.

Our client offers a competitive salary of £120,000–£140,000, plus 40% bonus, 40% LTIP, £7.5k car allowance, and a comprehensive benefits package. The role is hybrid, based in Wolverhampton or Chatham.

Key Responsibilities

Lead, develop, and inspire a high-performing Data Engineering team, setting the vision, priorities, and best practices.
Oversee the design, build, and optimisation of data ingestion, processing, and storage pipelines.
Embed strong governance, documentation, and lifecycle management standards across the function.
Partner with IT, architecture, and business stakeholders to ensure secure, scalable cloud configurations.
Champion innovation, exploring new tools, technologies, and approaches to enhance capability and efficiency.Core Requirements

Proven leadership experience managing a Data Engineering function of ~40 engineers (SQL and Azure).
Recent, hands-on experience implementing event-driven architecture using Kafka (essential).
Extensive background in regulated industries (finance or banking preferred).
Strong experience in large-scale data engineering projects, designing and developing ETL pipelines and leading cloud migration initiatives.
Expertise in data migration from SQL Server to Azure Cloud Services

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Where to Advertise Data Science Jobs in the UK (2026 Guide)

Advertising data science jobs in the UK requires a different approach to most technical hiring. Data science spans a broad and often misunderstood spectrum — from statistical modelling and experimental design through to machine learning engineering, product analytics and AI research. The strongest candidates identify firmly with specific subdisciplines and are frustrated by adverts that conflate data scientist with data analyst, business intelligence developer or machine learning engineer. General job boards produce high application volumes for data roles but consistently fail to match specialist data science profiles with the right opportunities. This guide, published by DataScienceJobs.co.uk, covers where to advertise data science roles in the UK in 2026, how the main platforms compare, what employers should expect to pay, and what the data says about hiring across different role types.

New Data Science Employers to Watch in 2026: UK and International Companies Leading Analytics and AI Innovation

Data science has emerged as one of the most transformative forces across industries, turning raw information into actionable insights, predictive models, and AI-powered solutions. In 2026, the UK is witnessing a surge in organisations where data science is not just a support function but the core of their products and services. For professionals exploring opportunities on www.DataScience-Jobs.co.uk , identifying these employers early can provide a competitive advantage in a market with high demand for advanced analytics and machine learning expertise. This article highlights new and high-growth data science employers to watch in 2026, focusing on UK startups, scale-ups, and global firms expanding their data science operations locally. All of the companies included have recently raised investment, won high-profile contracts, or significantly scaled their analytics teams.

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.