Azure Data Engineer

Reading
1 year ago
Applications closed

Related Jobs

View all jobs

Azure Data Engineer

Azure Data Engineer

Azure Data Engineer

Azure Data Engineer

Azure Data Engineer

Azure Data Engineer

Azure Data Engineer

A top Managed Services Provider are on the lookout for an Azure Data Engineer to join their expanding applications team. In this role, you'll support a crucial reporting project and play a key part in driving business transformation with the latest Microsoft cloud technologies.

The ideal candidate will have extensive experience as a data engineer within large organisations and enterprise platforms.

This opportunity is ideal for someone who enjoys staying up-to-date with technology through active involvement in solution delivery and is keen to advance their career by enhancing their skills in digital transformation.

Key Responsibilities:

Collaborate on enterprise-level projects.
Deliver hands-on solutions to internal clients.
Stay updated with technology trends and actively participate in solution delivery.Ideal Candidate:

Technical Skills:

Proficiency in Azure Data Platform (Data Lake, Synapse Analytics, SQL Database, Data Factory).
Strong knowledge of Power BI and Microsoft reference architectures.
Experience in Data Platform Design and Azure DevOps.
Familiarity with Dynamics 365 ERP and CRM data models.
Understanding of Azure data integration technologies and Modern Workplace.Experience:

3+ years in Data & Analytics.
Agile delivery methods.
Mentoring junior staff.
Deploying Azure solutions using CI/CD pipelines.
Data integration, analysis, modelling, cleansing, and enrichment.
Large-scale ERP/CRM implementations.
Working with remote teams.
Proficiency in SQL and Python.
Knowledge of Data Governance, including MDM and Data Quality tools.Remote based.

Up to 60k basic + good benefits

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 Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

Jobs

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

Education

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.

🍪 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.