Data Engineer

Tria
Wolverhampton
2 months ago
Applications closed

Related Jobs

View all jobs

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Are you a Data Engineer who wants to build the data foundations of an elite football club?

If you're strongest when you're turning messy scripts into reliable pipelines, automating workflows, and designing cloud data systems that people actually trust - this role will feel like a step up, not a sideways move.

This is a core hire within a Football Data Team, working day-to-day with performance analysts, data scientists, and technical staff across the first team. You'll own key data pipelines, shape how data is engineered long term, and build systems that directly support on-pitch decision-making.

Why this role matters

Data is moving from isolated analysis into core football operations.

This role sits at the centre of that shift. You won't just maintain pipelines - you'll help define:

How data is ingested, modelled, and deployed

How reliable, production-ready data supports performance insights

How multiple football data sources are unified into one trusted platform

You'll have real ownership, visibility, and influence over how the data function evolves.

What you'll be responsible for

Building, testing, and maintaining cloud-based ETL pipelines on AWS, ingesting data from APIs, web sources, and internal systems into Snowflake

Refactoring and optimising Python and SQL workflows for speed, reliability, and scalability

Automating pipelines and maintaining CI/CD, Git-based version control, and deployment standards

Improving data modelling, storage efficiency, schema design, and partitioning to support scalable analysis

Supporting the development of internal data tools and applications (e.g. Streamlit, Dash, or React-based apps)

Integrating structured data with video and performance workflows

Acting as the technical point of contact for external data providers

Maintaining strong data governance, security, and GDPR-compliant practices

What we're looking for

This role suits someone who is hands-on, curious, and comfortable owning production systems.

Essential experience:

Strong Python with demonstrable SQL

Experience building and maintaining AWS-based data pipelines (Lambda, S3, Glue, Step Functions or similar)

Experience working with Snowflake

Experience with CI/CD and Git

Comfortable working closely with analysts across multiple departments

2+ years' experience in a data engineering (or similar) role

Nice to have:

Experience with football or sports datasets

Experience with R

Experience building data models for reporting tools

Experience developing internal data applications

Familiarity with GDPR and data governance best practice

The environment

A collaborative, in-person football environment

Working closely with analysts, data scientists, and performance staff

Strong personalities, high standards, and a genuine interest in the game

A culture that values being progressive, humble, determined, bright, and unified

Safeguarding, equality, and inclusion are central to how the organisation operates, and full training and support will be provided

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.

Products

Where to Advertise Machine Learning Jobs in the UK (2026 Guide)

Advertising machine learning jobs in the UK requires a different approach to most technical hiring. The candidate pool is small, highly specialised and in demand across AI labs, financial services, healthcare, autonomous systems and consumer technology simultaneously. Machine learning engineers and researchers move between roles through professional networks, conference communities and specialist platforms — not general job boards where ML roles compete with unrelated software engineering positions for the same audience. This guide, published by MachineLearningJobs.co.uk, covers where to advertise machine learning 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.

Jobs

New Machine Learning Employers to Watch in 2026: UK and Global Companies Driving ML Innovation

Machine learning (ML) has transitioned from a specialised field into a core business capability. In 2026, organisations across healthcare, finance, robotics, autonomous systems, natural language processing, and analytics are expanding their machine learning teams to build scalable intelligent products and services. For professionals exploring opportunities on www.MachineLearningJobs.co.uk , understanding the companies that are scaling, winning investment, or securing high‑impact contracts is crucial. This article highlights the new and high‑growth machine learning employers to watch in 2026, focusing on UK innovators, international firms with significant UK presence, and global platforms investing in machine learning talent locally.

Careers

How Many Machine Learning Tools Do You Need to Know to Get a Machine Learning Job?

Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more. With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive. Here’s the honest truth most machine learning hiring managers won’t say out loud: 👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know. Tools are important — no doubt — but context, judgement and outcomes matter far more. So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped. This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.