Azure Data Engineer

KDR Talent Solutions
Manchester
10 months ago
Applications closed

Related Jobs

View all jobs

Azure Data Engineer

Azure Data Engineer

Azure Data Engineer

Azure Data Engineer / BI Developer

Azure Data Engineer

Azure Data Engineer & BI Specialist for Data Pipelines

This range is provided by KDR Talent Solutions. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.

Base pay range

Direct message the job poster from KDR Talent Solutions

Data & Analytics Recruiter - Supporting companies in hiring the best professionals in Data, Digital & Analytics

Azure Data Engineer | Location: Manchester (Hybrid - 1-2 days a month in office) | Salary: £55,000-£65,000

The Opportunity

Are you passionate about building cutting-edge data platforms that drive business growth? Our client is seeking a skilled and motivatedData Engineerto play a key role in the creation of abrand-new data platformwithin theMicrosoft Azure and Fabricecosystem.

This is an exciting opportunity to be at the forefront of data innovation, working within a newly formedData & Reportingteam. You’ll help shape the data strategy, improve data quality, and empower the business to make data-driven decisions.

As a Data Engineer, you'll work closely with both technical and business stakeholders, leveraging your expertise to design, develop, and optimize ahigh-performance data platform. This platform will be built to scale, incorporating the latest advancements in data intelligence while supporting strategic business objectives.

Key Responsibilities

  1. Build & Develop– Design and maintain a robustAzure-based Data Platform, ensuring performance, scalability, and availability.
  2. Data Pipelines– Connect APIs, databases, and data streams to the platform, implementing ETL/ELT processes.
  3. Data Integrity– Embed quality measures, monitoring, and alerting mechanisms.
  4. CI/CD & Automation– Create deployment pipelines and automate workflows.
  5. Collaboration– Work with stakeholders acrossGlobal IT, Data, and Reportingteams to translate business requirements into technical solutions.
  6. Futureproofing– Drive the evolution of the data platform, ensuring adaptability for new data sources, analytical models, and emerging technologies.

What You’ll Bring

  1. Extensive hands-on experiencewithMicrosoft Azure data tools(must-have: Azure Data Factory, Azure Synapse, or Azure SQL).
  2. Dimensional modellingexpertise for analytics use cases.
  3. Python scriptingexperience for data automation.
  4. Experience withCI/CD methodologiesfor data platforms.
  5. Knowledge ofMS SQL Server, SSIS, Visual Studio, and SSDT projects.
  6. Hands-on experience withMicrosoft Fabric.
  7. Familiarity withSalesforceand/orWorkday.
  8. Previous experience in a relevant industry.

Why Join?

  1. Greenfield Project– Work on an all-new data platform, shaping its architecture from the ground up.
  2. Collaborative Culture– Engage with global teams in an agile, innovative environment.
  3. Career Growth– Play a pivotal role in driving data excellence within a forward-thinking business.
  4. Cutting-Edge Tech– Work with the latest advancements inAzure, Fabric, and Data Engineering.

This is a fantastic opportunity for aData Engineerlooking to make a tangible impact. If you’re ready to take on a challenging and rewarding role, apply today!

Seniority level

Mid-Senior level

Employment type

Full-time

Job function

Information Technology

Industries

Data Infrastructure and Analytics, IT System Data Services, and IT Services and IT Consulting

#J-18808-Ljbffr

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.

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.

Machine Learning Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Are you considering a career change into machine learning in your 30s, 40s or 50s? You’re not alone. In the UK, organisations across industries such as finance, healthcare, retail, government & technology are investing in machine learning to improve decisions, automate processes & unlock new insights. But with all the hype, it can be hard to tell which roles are real job opportunities and which are just buzzwords. This article gives you a practical, UK-focused reality check: which machine learning roles truly exist, what skills employers really hire for, how long retraining realistically takes, how to position your experience and whether age matters in your favour or not. Whether you come from analytics, engineering, operations, research, compliance or business strategy, there is a credible route into machine learning if you approach it strategically.

How to Write a Machine Learning Job Ad That Attracts the Right People

Machine learning now sits at the heart of many UK organisations, powering everything from recommendation engines and fraud detection to forecasting, automation and decision support. As adoption grows, so does demand for skilled machine learning professionals. Yet many employers struggle to attract the right candidates. Machine learning job adverts often generate high volumes of applications, but few applicants have the blend of modelling skill, engineering awareness and real-world experience the role actually requires. Meanwhile, strong machine learning engineers and scientists quietly avoid adverts that feel vague, inflated or confused. In most cases, the issue is not the talent market — it is the job advert itself. Machine learning professionals are analytical, technically rigorous and highly selective. A poorly written job ad signals unclear expectations and low ML maturity. A well-written one signals credibility, focus and a serious approach to applied machine learning. This guide explains how to write a machine learning job ad that attracts the right people, improves applicant quality and strengthens your employer brand.