Senior Data Platform Engineer

About Football Radar

Football Radar has been developing statistical models and analytical frameworks for football for more than 10 years. We provide advice to football clubs and are also a leading provider of betting advice. We combine the agility and ownership of a start-up with the stability of an established, profitable business.

About the Role

Everything we do runs on data. Our models and research are only as good as the datasets behind them, so we are hiring a data platform engineer to help build and operate that foundation: the shared pipelines, storage systems, tools and cloud infrastructure that turn large, messy football datasets into reliable, affordable and accessible inputs for our data scientists. This is a data platform role grounded in software and cloud engineering. You will build both the systems that process our data and the reusable capabilities that make those systems easier to develop, deploy and operate. Much of the work will be familiar to a data engineer, however we tend to build directly in AWS rather than using managed platforms, so the role involves more hands-on cloud work than many data engineering roles. The role could suit a platform, backend or cloud engineer who wants to specialise in data-intensive systems. It could also suit a data engineer with strong software engineering fundamentals and hands-on experience building and operating cloud infrastructure. You will work on real football data problems involving rich, granular datasets that few companies have access to. We are a small team with short feedback loops, and you will work closely with the people who use what you build. You will have a genuine say in how systems should work, then build, ship and own them in production.

What You Will Work On

• Building reusable tools and pipelines for ingesting large third-party datasets, some at terabyte scale.
• Designing storage patterns and data interfaces that make datasets economical to store, and efficient to use.
• Operating batch workloads on AWS, with responsibility for their reliability, resource use, retry behaviour, observability and cost.
• Improving the shared tooling and infrastructure used to test, deploy, monitor and operate our data services.
• Making common data workflows easier and safer for data scientists, while working closely with them to understand how datasets will actually be used.
• Making nightly data loads and copy processes boring: restart-safe, observable and quick to recover when something fails.

What We Are Looking For

• Strong production software engineering: You have a few years of experience building production software, ideally in Python, although strong engineers from other languages who want to work in Python are welcome. You write code that is tested, deployed and monitored, and you are comfortable reading and debugging code written by other people.
• Hands-on cloud engineering: You have built, deployed and operated systems using cloud primitives directly, not only through a data platform such as Databricks or Snowflake. Your experience might include object storage, containerised compute, batch jobs, queues, identity and permissions, networking, monitoring or infrastructure as code. We use AWS for almost everything, but strong fundamentals on another public cloud transfer well. You do not need to know every AWS service we use. You should understand the core building blocks and their cost models well enough to design with them, see how the components of a cloud system fit together and investigate them when they fail.
• Strong systems fundamentals: You can reason about what the tools are doing underneath: how a database uses an index, why a process runs out of memory, how files and objects behave in cloud storage, and what makes a distributed job safe to retry or restart halfway through.
• Production ownership: You have kept something running that mattered. You have handled failures, added missing alerts and made unreliable systems dependable. You take pride in systems that are uneventful to operate.
• Data and SQL fundamentals: You are comfortable with SQL and relational databases. You can reason about schemas, indexes, query plans and the trade-offs involved in storing and processing large datasets.
• Comfort with loosely defined problems: Much of our work has no detailed specification or established playbook. You enjoy turning a vague problem into a practical design, making sensible choices and shipping a working system. You can judge when a detail deserves careful treatment and when a simple first version is the right answer.
• Curiosity about how data is used: You do not need a data science background, but our platform exists to serve statistical models and research. You should be interested in how datasets will actually be used and willing to let that shape the systems you build. An interest in football, sport or betting markets is a bonus.