Data Engineer

Huxley
London
7 months ago
Applications closed

Related Jobs

View all jobs

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Senior Data Engineer

London, UK

About the Role

We're looking for a Senior Data Engineer to lead the design and delivery of modern, cloud-based data solutions. You'll play a key role in transforming enterprise data platforms, enabling global data alignment, and driving business value through scalable architecture and advanced analytics.

Key Responsibilities

  • Design and implement end-to-end data pipelines using Azure and Databricks.
  • Modernize legacy data warehouses and unify data across global regions.
  • Optimize performance, cost, and scalability of data platforms.
  • Mentor junior engineers and promote best practices.
  • Collaborate with cross-functional teams to translate business needs into technical solutions.

Core Skills

  • Cloud & Platforms: Azure, AWS, SAP
  • Data Engineering: ELT, Data Modeling, Integration, Processing
  • Tech Stack: Databricks (PySpark, Unity Catalog, DLT, Streaming), ADF, SQL, Python, Qlik
  • DevOps: GitHub Actions, Azure DevOps, CI/CD pipelines

Please click here to find out more about our Key Information Documents. Please note that the documents provided contain generic information. If we are successful in finding you an assignment, you will receive a Key Information Document which will be specific to the vendor set-up you have chosen and your placement.

To find out more about Huxley, please visit www.huxley.com

Huxley, a trading division of SThree Partnership LLP is acting as an Employment Business in relation to this vacancy | Registered office | 8 Bishopsgate, London, EC2N 4BQ, United Kingdom | Partnership Number | OC387148 England and Wales


#J-18808-Ljbffr

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 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.

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.

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.