Lead Data Engineer - Microsoft Fabric - Hybrid - £75k

Winchester
2 months ago
Applications closed

Related Jobs

View all jobs

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer - Microsoft Fabric - Hybrid - Winchester - £75k

About the Role

Are you ready to lead the charge in modern data engineering? We're looking for a Senior Microsoft Fabric Data Engineer Consultant with 5+ years of experience to design and deliver cutting-edge cloud data platforms for our clients.

In this role, you'll be at the heart of transforming legacy systems into scalable, production-grade architectures using Microsoft Fabric. You'll build complex ingestion pipelines, craft metadata-driven transformation layers, optimise data workflows, and deliver high-quality semantic models that power enterprise reporting.

You'll work across Microsoft Fabric, Power BI, Power Platform, Dynamics 365, and Business Central, helping organisations embrace best-practice cloud engineering patterns. This is a hands-on, client-facing role where your ability to lead technical conversations and deliver robust, maintainable solutions will set you apart.

Key Responsibilities

Lead the design and build of end-to-end data solutions in Microsoft Fabric.
Migrate on-prem data to Fabric Lakehouse/Warehouse, enabling Power BI analytics.
Develop robust data pipelines, notebooks, and data models using:
Python, PySpark, SQL, Synapse, and Data Warehousing principles.
Implement CI/CD processes with GitHub integration for lifecycle management.
Ensure governance, security, and best practices across environments.
Act as the primary technical contact for stakeholders, running workshops and guiding the team.
Contribute to pre-sales and wider business opportunities where relevant.Essential Skills

5+ years in Data Engineering, ideally within Azure ecosystems.
Strong experience with:
Python, Notebooks, Synapse, SQL, Data Warehousing.
Microsoft Fabric (Lakehouse, Pipelines, Warehouse) - deep knowledge preferred.
Proven ability to implement CI/CD pipelines and GitHub integration.
Excellent client-facing communication and ability to lead technical discussions.
Appreciation of AI capabilities and emerging trends.
Desirable

Experience in insurance or financial services.
Familiarity with Power BI, DAX, and semantic modelling.
Exposure to Azure DevOps, GitHub Actions, and deployment pipelines.
Microsoft certifications (DP-600, DP-203, DP-700) or equivalent experience.Why Apply?

High-impact project with full ownership of technical delivery.
Opportunity to define data strategy for a new business division.
Flexible hybrid working - remote-first with occasional office presence.
Potential to transition into a Data Lead role.
Hybrid - ideally 1-2 days per week in office.

Interested? Apply now

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 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.

Jobs

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.

Education

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.

🍪 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.