Fabric Data Engineer

Winchester
2 months ago
Applications closed

Related Jobs

View all jobs

Fabric Data Engineer

Data Engineer

Data Engineer (SC Cleared)

Senior Data Engineer

Data Engineer

Data Engineer

Senior Data Engineer - Microsoft Fabric

Location: Hybrid (UK) - Hampshire
Type: Permanent
Salary: Up to £75,000

About the Role
We're looking for an experienced Senior Data Engineer with deep expertise in Microsoft Fabric to design and deliver modern cloud data platforms for enterprise clients. This role involves building scalable architectures, optimising data pipelines, and creating high-quality semantic models to support advanced analytics and reporting.

You'll work across Microsoft Fabric, Power BI, and related technologies, enabling organisations to modernise legacy systems and adopt best-practice data engineering patterns. This is a hands-on, client-facing role where you'll lead technical conversations and deliver robust, maintainable solutions.

Key Responsibilities

Design end-to-end data architectures using Medallion (Bronze/Silver/Gold) patterns
Build metadata-driven ingestion pipelines and transformation frameworks
Develop advanced PySpark/Spark SQL notebooks for data cleansing and modelling
Create production-ready semantic models and support BI teams
Implement governance, security, and CI/CD best practices
Engage with clients to translate business requirements into technical solutionsEssential Skills

Strong experience with Microsoft Fabric workloads (Lakehouse, Data Factory, Pipelines, Notebooks, Semantic Models)
Advanced PySpark/Spark SQL for large-scale transformations
Integration with Dynamics 365, Dataverse, and/or Business Central
CI/CD implementation using Azure DevOps or GitHub
Solid understanding of dimensional modelling and Power BI optimisationDesirable

Microsoft Fabric certifications
Familiarity with Azure services and Power Platform
Experience in consultancy environments and large enterprise data estates

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.

How Many Data Engineering Tools Do You Need to Know to Get a Data Engineering Job?

If you’re aiming for a career in data engineering, it can feel like you’re staring at a never-ending list of tools and technologies — SQL, Python, Spark, Kafka, Airflow, dbt, Snowflake, Redshift, Terraform, Kubernetes, and the list goes on. Scroll job boards and LinkedIn, and it’s easy to conclude that unless you have experience with every modern tool in the data stack, you won’t even get a callback. Here’s the honest truth most data engineering hiring managers will quietly agree with: 👉 They don’t hire you because you know every tool — they hire you because you can solve real data problems with the tools you know. Tools matter. But only in service of outcomes. Jobs are won by candidates who know why a technology is used, when to use it, and how to explain their decisions. So how many data engineering tools do you actually need to know to get a job? For most job seekers, the answer is far fewer than you think — but you do need them in the right combination and order. This article breaks down what employers really expect, which tools are core, which are role-specific, and how to focus your learning so you look capable and employable rather than overwhelmed.

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

If you’re applying for data engineering jobs in the UK, the first thing to understand is this: Hiring managers don’t read every word of your CV. They scan it. They look for signals of relevance, credibility, delivery and collaboration — and if they don’t see the right signals quickly, your application may never get a second look. In data engineering, hiring managers are especially focused on whether you can build and operate reliable, scalable data systems, handle real-world data challenges and work effectively with analytics, BI, data science and engineering teams. This guide breaks down exactly what they look at first in your application — and how to shape your CV, portfolio and cover letter so you stand out.

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

Data engineering has quietly become one of the most critical roles in the modern technology stack. While data science and AI often receive the spotlight, data engineers are the professionals who design, build and maintain the systems that make data usable at scale. Across the UK, demand for data engineers continues to rise. Organisations in finance, retail, healthcare, government, media and technology all report difficulty hiring candidates with the right skills. Salaries remain strong, and experienced professionals are in short supply. Yet despite this demand, many graduates with degrees in computer science, data science or related disciplines struggle to secure data engineering roles. The reason is not academic ability. It is a persistent skills gap between university education and real-world data engineering work. This article explores that gap in depth: what universities teach well, what they consistently miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data engineering.