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

Apply Now

Data Engineer - Databricks & Azure Technologies - Clean Energy

Data Science Talent
7 months ago
Applications closed

Related Jobs

View all jobs

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer - Databricks & Azure Technologies - Clean Energy


Location:South East England (Hybrid - 1 day onsite per week)


Salary:£65 - 70k + benefits package


18 months. That’s all the time it took for the client’s Databricks platform to evolve into a key driver of innovative green technologies. Now, they’re looking for someone to take it even further.


Imagine joining a forward-thinking client at the forefront of clean energy innovation. Your work will directly contribute to a zero-carbon future by supporting advancements in electrolysis for green hydrogen production and fuel cells for future power solutions. Through powerful partnerships with major multinational companies, the client’s solid oxide platform is transforming energy systems and helping decarbonise emissions-heavy industries like steelmaking and future fuels.


What’s the Role?


You’ll join a highly skilled data team, part of a broader department focused on modelling and digitalisation. This team develops and maintains a cutting-edge Azure Databricks Data Lakehouse platform to support all core business functions. Your primary goal will be building and maintaining robust, secure data pipelines and models that deliver trusted datasets to internal and external stakeholders, enabling data-driven decisions across the organisation.


As a Data Engineer, you will maintain, monitor, and enhance the Databricks platform that powers the client’s data services. You’ll work on building robust pipelines using Azure Data Lake and Python while collaborating closely with data scientists, simulation engineers, and the wider business.


Reporting to the Head of Data Management, you’ll be a part of a collaborative team focused on data governance, engineering, and strategy. This role offers the chance to make a visible impact in a dynamic, fast-evolving field.



Why Join?


  • IMPACTFUL WORK: Help revolutionise electrolyzer technology, accelerating clean hydrogen production and decarbonisation on a global scale.


  • SEE RESULTS QUICKLY: Your work will directly influence live projects, delivering measurable results in real-world applications.


  • CULTURE OF INNOVATION: Collaborate with forward-thinking professionals in an environment where experimentation and creativity are encouraged.


  • SECURE GROWTH: Join a financially robust organisation investing heavily in cutting-edge technologies and talent development.


  • PURPOSE-DRIVEN MISSION: Be part of a team dedicated to advancing green technologies and creating a sustainable future.



What You Can Add


We’re looking for someone who thrives on solving complex problems and working in fast-paced environments. Here’s what you’ll need:


  • 18 months or more of Databricks experience, with a strong background in managing and maintaining data services on the platform.


  • Expertise inAzure Data Lake,Python, andCI/CD pipelinesusing Azure DevOps.


  • Practical experience in industries likemanufacturing,product development, orautomotive, with a focus on real-world applications.


  • Familiarity with data modelling, governance, and digitisation.


  • Bonus: Knowledge ofUnity Catalogin Databricks.



Ready to shape the future of clean energy? Apply now to join our client as a Data Engineer and help drive the green energy revolution.

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.