Lead Data/Head of Data Engineer

Reading
1 month ago
Applications closed

Related Jobs

View all jobs

Senor / Lead Data Engineer

Lead Data Scientist / Deep Learning Practitioner

Lead Data Scientist / Deep Learning Practitioner

Lead Data Scientist - Deep Learning Practitioner

Lead Data Scientist / Deep Learning Practitioner

Lead Data Scientist - Deep Learning Practitioner

Lead Data Engineer/Head of Data

Permanent

On behalf of a fantstic cleint we are resourcing for the following role

This is a senior, hands-on technical leadership role reporting directly to the CTO. You'll shape and deliver a modern data and AI platform, lead a small team of data and analytics engineers, and embed machine learning, AI agents, and advanced analytics into real customer workflows.

The Role

You'll own the end-to-end data and AI capability - from platform architecture through to production ML systems - ensuring data and AI are applied thoughtfully, responsibly, and with clear business impact.

What You'll Do

Design and evolve a secure, scalable data & AI platform with Snowflake at its core

Build production-grade data pipelines, models, and data products for analytics and AI use cases

Design, train, and deploy ML models, embeddings, and vector stores to enable AI-driven experiences

Lead and mentor a small, high-impact team of data and analytics engineers

Partner closely with Product, Engineering, and Infrastructure teams

Set standards for data quality, governance, security, and performance

Act as a trusted technical advisor to the CTO and senior leadership

What We're Looking For

Essential

Expert-level Snowflake experience (modelling, optimisation, advanced features)

Strong Python skills across data engineering, ML, and AI development

Proven experience delivering production ML systems

Hands-on experience with embeddings, vector databases, and LLM-driven systems

Deep understanding of modern data engineering practices (ELT, orchestration, versioning)

Nice to Have

Background in data science or applied ML

Experience building AI agents or intelligent automation

Familiarity with cloud-native architectures and MLOps

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.

Products

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.

Jobs

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.

Careers

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.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.