Data Engineer
Silicon Fen Resourcing
Maidstone, United Kingdom
7 days ago
Role details
Contract type
Permanent contract Employment type
Full-time (> 32 hours) Working hours
Regular working hours Languages
English Compensation
£ 75KJob location
Remote
Maidstone, United Kingdom
Tech stack
Azure
Data Architecture
Data Governance
ETL
Data Systems
Microsoft Software
SQL Databases
Microsoft Fabric
Data Management
Data Pipelines
Job description
This is a great opportunity for a Data Engineer who enjoys designing scalable data solutions and working with the latest Microsoft technologies. You ll play a key role in developing a new data platform that modernises how data is ingested, transformed, and delivered across the organisation.
You ll be working within an ambitious data function that is moving towards Microsoft Fabric and a medallion-style architecture. The work is hands-on, technically interesting, and directly connected to real business outcomes.
What you ll be doing
- Building and enhancing data pipelines within Microsoft Fabric and the wider Azure ecosystem.
- Implementing a Bronze / Silver / Gold data architecture to standardise structure, quality, and consumption across key datasets.
- Working closely with analytics, digital, and operational teams to understand their requirements and deliver practical engineering solutions.
- Improving automation, performance, and reliability across the data platform.
- Contributing to patterns, documentation, and data standards that support a consistent engineering approach.
- Embedding good practice around data quality, lineage, and governance using Fabric-native capabilities.
Requirements
- A Data Engineer with hands-on experience in Microsoft cloud data platforms, Microsoft Fabric, or Azure-based technologies.
- Strong SQL skills and practical experience building ETL/ELT pipelines and scalable data models.
- Experience working with medallion-style architectures.
- A good understanding of data governance principles, documentation, and best practice.
- Someone who can work collaboratively, communicate clearly, and translate requirements into well-engineered data solutions.