Data Engineer | £60,000 - £65,000 | UK/Remote |

Opus Recruitment Solutions
London
1 year ago
Applications closed

Related Jobs

View all jobs

Senior Data Engineer - DV Clearance

Senior Data Engineer - Azure

Data Engineer

Azure Data Engineer | Hybrid, 30 Days Leave, £4k Training

Data Engineer

London Data Scientist & ML Engineer | AWS, Python

Data Engineer | £60,000 - £65,000 | UK/Remote |


Power BI | Synapse | Azure Data Bricks | ADF | SQL | Data Engineer | Reporting | Data Sets | Fabric |


Are you a Data Engineer who enjoys getting involved with the architecture side of the role? Or maybe you want to join a company and have autonomy? If so I have a role for you.


I am looking for an experienced Data Engineer to join a company that support the automotive industry covering a range of aspects from theft prevention to fleet management. They have grown impressively over the last year and are now looking to invest in people.


They have recently gone through an Azure Migration and are looking for a Data Engineer to join a small team to architect data sets, push them into Power BI and move them export to data lake to fabric link.


They are using a great tech stack which includes –Azure ADF, Data Lakes, Synapse, Power BI, Fabric, Logic Apps, SQL.


You will also be working closely with BI and SQL Developers with building dashboards and reports which will be going out to various customers.


Benefits include –


  • 5% company pension
  • Remote working
  • Salary up to £65,000
  • Private healthcare
  • Learning and Development budget
  • Perkbox
  • Life Assurance
  • Wellbeing options


Even though this is a remote position you need to be a full time UK resident and sponsorship isn’t available.


This a brilliant opportunity for someone looking to grow their career. They have a fantastic tech stack and have brilliant progression routes.


Power BI | Synapse | Azure Data Bricks | ADF | SQL | Data Engineer | Reporting | Data Sets | Fabric |

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.

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.

The Skills Gap in Machine Learning Jobs: What Universities Aren’t Teaching

Machine learning has moved from academic research into the core of modern business. From recommendation engines and fraud detection to medical imaging, autonomous systems and language models, machine learning now underpins many of the UK’s most critical technologies. Universities have responded quickly. Machine learning modules are now standard in computer science degrees, specialist MSc programmes have proliferated, and online courses promise to fast-track careers in the field. And yet, despite this growth in education, UK employers consistently report the same problem: Many candidates with machine learning qualifications are not job-ready. Roles remain open for months. Interview processes filter out large numbers of applicants. Graduates with strong theoretical knowledge struggle when faced with practical tasks. The issue is not intelligence or effort. It is a persistent skills gap between university-level machine learning education and real-world machine learning jobs. This article explores that gap in depth: what universities teach well, what they routinely miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in machine learning.