Azure Data Engineer

Softcat plc
Marlow
2 months ago
Applications closed

Related Jobs

View all jobs

Azure Data Engineer

Azure Data Engineer

Azure Data Engineer

Azure Data Engineer

Lead Data Engineer

Senior Data Engineer - Azure & Snowflake

Would you like to kick start your career in a supportive, collaborative and innovative company?


Do you enjoy working as part of an enthusiastic, passionate and collaborative team?


As one of the UK's leading IT infrastructure providers and a FTSE 250 listed company, we have built a reputation for excellence. Our strategy is simple – we believe that highly engaged employees are the key to building customer trust and loyalty over the years. This trust and loyalty, combined with our market leading growth and performance, enables us to invest in our technology and services capabilities.


Softcat is an amazing success story and as part of our continued growth we are investing significantly more in a new technology strategy going forward. Softcat's internal undergoing an exciting transformation; this evolution aims to provide greater opportunities for our people's professional development and prepare us to execute our more ambitious technology strategy effectively.


Introduction to Data


Data and analytics at Softcat are in transformation and acceleration with a focus on cloud technology, automation, reducing technical debt, foundational data management and controlled self-service alongside company‑wide strategic initiatives where data plays a major role in its success.


To support the exciting Data strategy at Softcat, the Data Services team are looking to continually improve all things related to Data and analytics for the next 3‑5 years. To support that, we are looking to expand the team.


As Azure Data Engineer, you'll be responsible for:



  • Design, develop, test, and maintain robust, reusable data pipelines using Azure Data Factory (orchestration), Azure Databricks (transformations in PySpark/Spark SQL), and DBT (SQL‑based modelling)
  • Prepare, clean, and transform unstructured and semi‑structured data for LLM training, fine‑tuning, and prompt engineering workflows
  • Develop Python‑based ETL/ELT scripts, data transformation utilities, and automation tools
  • Implement CI/CD pipelines using Azure DevOps and Databricks Asset Bundles for data workflows, promoting automation, reproducibility, and minimal manual intervention
  • Collaborate with Data Scientists, AI/ML Engineers, and Analysts to optimise the flow of data into ML and LLM models

We’d love you to have



  • Strong hands‑on experience with Azure Data Factory (pipelines, triggers, parameterisation, linked services)
  • Strong hands‑on experience with Azure Databricks (PySpark, Spark SQL, Delta Lake, performance tuning)
  • Strong SQL development skills, including performance tuning and working with large datasets
  • Proficiency with Python for data engineering tasks (e.g., Pandas, PySpark, data cleaning, API integrations)
  • Proficiency with DBT (data modelling, macros, testing, documentation)
  • Experience with Azure DevOps for Git‑based source control and deployment pipelines for data solutions

Work in a way that works for you


We recognise that everyone is different and that the way in which people want to work and deliver at their best is different for everyone too. In this role, we can offer the following flexible working patterns:



  • Working flexible hours – flexing the times you start and finish during the day
  • Flexibility around school pick up and drop offs

Working with us


Wherever you work, we want you to experience the freedom and autonomy to realise your potential. You will feel supported by a team that celebrates individuality, encourages different perspectives and embraces every background.


To become part of the success story, please apply now.


If you have a disability or neurodiversity, we can provide support or adjustments that you may need throughout our recruitment process or any mitigating circumstance you wish for us to consider. Any information you share on your application will be treated in confidence.


You can find out more about life at Softcat and our commitments to diversity and inclusion at jobs.softcat.com/jobs/our‑culture/


We offer a competitive salary and benefits package and will provide you with opportunities to grow, flourish, and achieve great things. Our benefits include:


Benefits

  • Pension
  • Share incentive plan
  • Life Assurance
  • Holiday
  • Trips
  • Vouchers
  • Partner/family Benefits
  • Maternity, Paternity and Adoption support


#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.

Careers

How Many Machine Learning Tools Do You Need to Know to Get a Machine Learning Job?

Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more. With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive. Here’s the honest truth most machine learning hiring managers won’t say out loud: 👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know. Tools are important — no doubt — but context, judgement and outcomes matter far more. So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped. This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.

Jobs

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.

Careers Jobs

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.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.