Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Data Engineer

Wave Talent
Birmingham
3 days ago
Create job alert

🚀 Data Engineer | Own the AI Foundation at a Leading Sports Tech Scale-Up


Location: Remote (UK) | Salary: DOE + Stock Options


The Mission: Solve a Critical AI Bottleneck


We are a rapidly growing, highly profitable Sports Technology scale-up—a market leader in our category—undergoing global expansion and a complete rebuild of our core platform.

We are seeking a dedicated, experienced Data Engineer to join our team as the sole, dedicated data expert. You will be the essential bridge between our Core Engineering team and our specialised AI/Machine Learning team.

Currently, our AI specialists are spending too much time on ETL and data wrangling. We need you to eliminate that bottleneck and provide the foundation for our next generation of predictive models. This is a role with full ownership and a clear mandate for architectural impact.


What You Will Own


  • Architectural Ownership: Design, build, and maintain scalable, robust data pipelines for high-volume, real-time data ingestion from multiple structured sports data feeds.
  • AI Enablement (MLOps Foundation): Partner with the Chief Scientific Officer and the AI team to deliver clean, structured, and model-ready datasets, enabling them to focus purely on complex modelling and feature engineering.
  • Scale & Performance: Optimise database performance and manage cloud data storage (GCP focus) to handle massive scale and support low-latency data services for our new user-facing product features.
  • Data Governance: Establish and champion best practices for data quality, consistency, monitoring, and documentation across the entire data lifecycle.


We Are Looking For


  • Experience: Proven, non-graduate experience designing and maintaining production data pipelines and ETL/ELT processes in a commercial environment.
  • Core Skills: Strong proficiency in Python and hands-on experience with cloud-based data storage and compute platforms (AWS, GCP, or Azure).
  • Data Expertise: Familiarity with relational and non-relational databases and experience deploying data APIs and microservices.
  • Critical Soft Skills: You must be an excellent communicator, highly conscientious, and capable of working independently to drive the entire data strategy.
  • Bonus: Experience with MLOps principles, Infrastructure as Code (e.g., Pulumi/Terraform), or working with sports/high-frequency data is highly beneficial.


Why Join Us?


  • Direct Impact: You will be the single most important hire for unlocking the potential of our AI team and accelerating our global product roadmap.
  • Cutting-Edge Stack: Work with modern technologies including GCP and React Native in a technically ambitious, agile environment.
  • Unrivalled Package: Base salary, plus generous Equity Options and benefits.
  • Flexibility: Enjoy the freedom of a Fully Remote role with flexible working hours.


We are aiming to move quickly and extend an offer before Christmas (Target Dec 12th).

If you are an ambitious Data Engineer ready to step into a role where your technical expertise directly translates into business advantage, apply now for a confidential discussion.

Related Jobs

View all jobs

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

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.

Machine Learning Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK machine learning hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise shipped ML/LLM features, robust evaluation, observability, safety/governance, cost control and measurable business impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for ML engineers, applied scientists, LLM application engineers, ML platform/MLOps engineers and AI product managers. Who this is for: ML engineers, applied ML/LLM engineers, LLM/retrieval engineers, ML platform/MLOps/SRE, data scientists transitioning to production ML, AI product managers & tech‑lead candidates targeting roles in the UK.

Why Machine Learning Careers in the UK Are Becoming More Multidisciplinary

Machine learning (ML) has moved from research labs into mainstream UK businesses. From healthcare diagnostics to fraud detection, autonomous vehicles to recommendation engines, ML underpins critical services and consumer experiences. But the skillset required of today’s machine learning professionals is no longer purely technical. Employers increasingly seek multidisciplinary expertise: not only coding, algorithms & statistics, but also knowledge of law, ethics, psychology, linguistics & design. This article explores why UK machine learning careers are becoming more multidisciplinary, how these fields intersect with ML roles, and what both job-seekers & employers need to understand to succeed in a rapidly changing landscape.

Machine Learning Team Structures Explained: Who Does What in a Modern Machine Learning Department

Machine learning is now central to many advanced data-driven products and services across the UK. Whether you work in finance, healthcare, retail, autonomous vehicles, recommendation systems, robotics, or consumer applications, there’s a need for dedicated machine learning teams that can deliver models into production, maintain them, keep them secure, efficient, fair, and aligned with business objectives. If you’re hiring for or applying to ML roles via MachineLearningJobs.co.uk, this article will help you understand what roles are typically present in a mature machine learning department, how they collaborate through project lifecycles, what skills and qualifications UK employers look for, what the career paths and salaries are, current trends and challenges, and how to build an effective ML team.