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

Apply Now

Xpertise Recruitment | Data Engineer

Xpertise Recruitment
Liverpool
9 months ago
Applications closed

Data Engineer - Consultancy - Liverpool (Flexible, hybrid working is encouraged)


Xpertise Recruitment is seeking a data engineer to join an established tech consultancy in Liverpool.


Why should you want to join?


  • You will be representing an award winning consultancy that are well-respected in the industry for taking on the biggest challenges and delivering excellent pieces of work.
  • You'll be working in an encouraging, fast-paced and productive environment with some of the best data engineers in the North-west supporting you along the way.
  • Deliver data platforms for industry leading companies and test yourself with a range of modern tools/technology (GCP, Azure, AWS, Databricks, Data Factory, Kafka etc.)
  • This consultancy operates on a hybrid working model and they have an excellent office space for collaboration with the team.
  • The base salary for this mid-level data engineering position goes up to £60k, and the benefits are what you would expect from any top company (bonus, loads of holidays, car scheme, support with qualifications, great pension, health and dental cover and much more!)


For more information, job specs or an initial conversation, please apply with an updated CV.

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.