Data Engineering Manager

McCabe & Barton
Glasgow
10 months ago
Applications closed

Related Jobs

View all jobs

Data Engineer

Data Scientist - London, UK (Fully REMOTE)

Python Data Engineer

Data Engineer

Data Engineer (Automation)

Mid Level Data Engineer

A leading Financial Services client in the City of London is now seeking an experienced Data Engineering Manager to join on a permanent basis. This role is offering a base of £85,000 + a strong benefits package and flexible working.


The ideal Data Engineering Manager will come from a data engineering background and have strong knowledge in SQL, Snowflake, Microsoft Azure, Azure Data Factory and Azure DevOps. The Engineering Manager will design, improve and maintain robust data pipelines within data architecture.


To be considered for this role you will need the following:


  • Experience designing, improving and maintaining robust data pipelines
  • Strong SQL programming skills. Knowledge of other programming languages such as Python, C++ and Java beneficial
  • Possesses a strong understanding of Snowflake - beneficial
  • Experience managing small teams of Data Engineers
  • Strong experience working in a cloud environment and knowledge in the following very beneficial: Microsoft Azure, Azure Data Factory and Azure DevOps
  • Experience working in fast-paced Agile environments
  • Creativity and curiosity for solving complex problems.


If you are an experienced Data Engineering Manager with the required skills, please respond with an up-to-date version of your CV for review.

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.