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

Apply Now

Data Engineer - Fabric

MBN Solutions
Southampton
6 months ago
Applications closed

Related Jobs

View all jobs

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer

Data Engineer (Consultant / Senior Consultant)

£60,000-£75,000 + Benefits

London, Manchester, Birmingham Locations Available


Looking for your next challenge inData Engineering? We’re seeking talented professionals to help businessesunlock the power of their datausing cutting-edge technologies.


🔹 Are you passionate aboutAzure-based data solutions?

🔹 Do you have expertise inSQL, Python, and modern data platforms?

🔹 Want to work onhigh-impact projectswith leading organizations?


💡What You’ll Do:

✅ Design and deliverdata solutionsusingFabric, Azure Data Factory, and Synapse

✅ Work withSQL, Python, Spark, Kafka, Snowflake, and more

✅ Apply best practices inData Architecture, Governance, and Engineering

✅ Collaborate with clients to drivereal business impact

✅ Be part of asupportive, high-performing team


🎯What We’re Looking For:

✔️ Strongdata engineering experienceinAzure

✔️ Hands-on expertise inSQL, Python, anddata pipeline development

✔️ Familiarity withAgile, DevOps, Git, APIs, and Cloud Data Platforms

✔️ A team player withproblem-solving skillsand aconsulting mindset

📜Bonus:DP-203 or Fabric Analytics certifications are a plus!


Apply today or DM us to learn more!

#DataEngineer #Hiring #Azure #SQL #Python #DataJobs

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 Hiring Trends 2026: What to Watch Out For (For Job Seekers & Recruiters)

As we move into 2026, the machine learning jobs market in the UK is going through another big shift. Foundation models and generative AI are everywhere, companies are under pressure to show real ROI from AI, and cloud costs are being scrutinised like never before. Some organisations are slowing hiring or merging teams. Others are doubling down on machine learning, MLOps and AI platform engineering to stay competitive. The end result? Fewer fluffy “AI” roles, more focused machine learning roles with clear ownership and expectations. Whether you are a machine learning job seeker planning your next move, or a recruiter trying to build ML teams, understanding the key machine learning hiring trends for 2026 will help you stay ahead.

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.