About this role
At Network Plus, our data transformation journey is well underway. Over the last 12 months, we have successfully migrated significant portions of our on-premises data warehouse and Power BI estate to Microsoft Fabric, establishing a modern, scalable data platform that supports enterprise reporting, analytics, and operational decision-making. Our Data Engineering team has built the foundations of a robust Fabric-based data platform, including automated ingestion pipelines, medallion architecture lakehouses, semantic models, and governance frameworks. As we continue to mature our platform and expand adoption across the business, we are looking for talented Data Engineers to help shape the next phase of our data strategy. This is an exciting opportunity to join a team that has moved beyond platform implementation and is now focused on optimisation, innovation, data quality, and delivering measurable business value through data. We are seeking a skilled Data Engineer with experience in Microsoft Fabric, Azure data technologies, and modern data warehouse architectures to join our growing Data Engineering team. You will play a key role in enhancing and expanding our enterprise data platform, supporting the migration of remaining legacy solutions, and ensuring that high-quality, trusted data is available across Network Plus. Working closely with business stakeholders, analysts, and technical teams, you will help deliver scalable and resilient data solutions that underpin critical business processes. Design, develop, and maintain data pipelines, lakehouses, and warehouse solutions within Microsoft Fabric. Support the ongoing migration and modernisation of legacy on-premises data warehouse and reporting solutions. Develop and optimise ETL/ELT processes using Fabric Data Factory, Notebooks, PySpark, SQL, and related technologies. Collaborate with Data Analysts, BI Developers, and business stakeholders to understand and deliver data requirements. Implement and maintain data quality, governance, security, and monitoring processes. Optimise platform performance, data models, and storage strategies to maximise efficiency and scalability. Support semantic models and reporting solutions that enable self-service analytics across the organisation. Troubleshoot and resolve data platform issues while continuously improving operational reliability. Contribute to data architecture standards, best practices, and technical documentation.