Fabric Data Engineer

Brentwood
3 months ago
Applications closed

Related Jobs

View all jobs

Fabric Data Engineer

Data Engineer (SC Cleared)

Senior Data Engineer

Data Engineer

Data Engineer

Data Engineer (Outside IR35)

BI Developer - London - £65,000

Are you passionate about building scalable BI solutions and working with cutting-edge data technologies? We are seeking a BI Developer to join a dynamic team and help shape the future of data analytics within a global organisation.

About the role:

Design and develop BI solutions using Microsoft Fabric and related technologies.
Build and manage data pipelines leveraging Data Factory.
Develop semantic models in Power BI
Collaborate with data architects, analysts, and stakeholders to deliver actionable insights.
Optimise data models for performance and reusability.
Support governance, security, and compliance best practices.Key Responsibilities

Deliver scalable Azure-based data platforms, including Data Warehouses and reporting tools.
Provide technical support and manage a modern technology stack (Azure Synapse, SSIS, SQL, Data Lake).
Assist with migration and reconciliation of data from legacy systems or acquisitions.
Act as a hands-on Data Engineer across the full stack.Requirements:

Strong expertise in SQL, Power BI, and cloud platforms.
Experience with Microsoft Fabric
Excellent communication skills to engage with technical and non-technical teams.Benefits:

Work with cutting-edge technologies in a modern data platform environment.
Be part of a collaborative team driving innovation and insight.
Competitive salary and benefits package

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.