Perm - Azure Data Engineers

Reed
Huddersfield
1 year ago
Applications closed

Related Jobs

View all jobs

Senior Machine Learning Engineer

Data Engineering Manager

Data Engineering Manager

Computer Vision Engineer

Data Analyst (Permanent/FTC)

Data Engineer

Azure Data Engineer Annual Salary: £48,341.06 Location: Huddersfield, HD1 Job Type: Permanent My client are looking for an innovative and creative Azure Data Engineer to join their team. The successful candidate will be instrumental in designing, developing, and delivering data products and services that support business intelligence, analytics, and insights across the Council’s services. Day to Day of the Role: Contribute to the success of the Data and Insight Service by supporting services and decision-makers with valuable insights. Collaborate within and between council services and partner organisations to improve data capability and foster a culture of continuous improvement. Lead the design, development, and implementation of automated data flows and database management. Write ETL and ELT scripts and code to ensure optimal performance. Build accessible data models for analysis and maintain metadata repositories. Implement testing regimes to monitor data engineering work and resolve problems promptly. Document source-to-target mappings and act as a mentor to SQL Developers. Deputise for the Data Engineering and Development Lead as required. Required Skills & Qualifications: Undergraduate degree with a strong data component (e.g., Computer Science, Engineering, Statistics) or equivalent experience. Proven experience in designing, building, and testing complex or large-scale data products and services. Advanced skills in SQL development and experience with SQL Server Integration Services (SSIS). Experience with cloud-based technology and services within a Microsoft Azure environment. Proficiency in programming languages such as Python, C#, or Scala. Knowledge of data modelling concepts and principles, and experience in producing data models. Experience in implementing data standards, data quality rules, and CI/CD practices. Ability to work with both technical and non-technical stakeholders to gather and analyse requirements. Experience in developing automated, repeatable, and scalable data flows. Willingness to work in an agile development environment. Benefits: Competitive salary with annual pay reviews. Opportunities for personal and professional development. Agile and innovative working environment. Potential for line-management responsibilities. Please apply today for a chance of an immediate interview

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 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.

Maths for Machine Learning Jobs: The Only Topics You Actually Need (& How to Learn Them)

Machine learning job adverts in the UK love vague phrases like “strong maths” or “solid fundamentals”. That can make the whole field feel gatekept especially if you are a career changer or a student who has not touched maths since A level. Here is the practical truth. For most roles on MachineLearningJobs.co.uk such as Machine Learning Engineer, Applied Scientist, Data Scientist, NLP Engineer, Computer Vision Engineer or MLOps Engineer with modelling responsibilities the maths you actually use is concentrated in four areas: Linear algebra essentials (vectors, matrices, projections, PCA intuition) Probability & statistics (uncertainty, metrics, sampling, base rates) Calculus essentials (derivatives, chain rule, gradients, backprop intuition) Basic optimisation (loss functions, gradient descent, regularisation, tuning) If you can do those four things well you can build models, debug training, evaluate properly, explain trade-offs & sound credible in interviews. This guide gives you a clear scope plus a six-week learning plan, portfolio projects & resources so you can learn with momentum rather than drowning in theory.