Data Engineer

Sword is a leading provider of business technology solutions within the Energy, Public and Finance Sectors, dedicated to driving transformational change for our clients. We leverage proven technology, specialist teams, and extensive domain expertise to create robust technical foundations across platforms, data, and business applications. Our mission is fueled by a passion for technology as a means to solve complex business problems and achieve our clients' objectives. About the role: This Data Engineer role offers an excellent opportunity for someone with a good understanding and working knowledge of designing and building robust data pipelines, integrating complex data sources, and developing solutions using Azure Data Services, Microsoft Fabric, Power BI, and Purview. As an experienced Data Engineer, you will handle more advanced engineering tasks, drive improvements to existing pipelines, and contribute to the design of scalable, high quality data architectures. You’ll collaborate directly with clients and cross-functional teams, translating real business needs into effective data solutions while beginning to support and mentor junior engineers. This role is ideal for someone who wants to grow toward senior-level responsibilities, gain exposure to architectural thinking, and work with cutting-edge technologies, including Microsoft Foundry as part of emerging AI-driven capabilities. Candidates should apply if they’re looking for a role that blends hands-on engineering, problem-solving, continuous learning, and meaningful contribution to client outcomes within a high-performing Data & AI business unit. A Data Engineer is responsible for several key activities: Design, build, and maintain scalable, secure, and high performing data pipelines using Azure Data Factory, Synapse, Fabric Pipelines, or Databricks Integrate data from multiple systems into Azure Data Lake Storage or OneLake, ensuring consistency, reliability, and performance Develop and maintain ELT/ETL processes using Azure-native tools and Fabric capabilities, optimising for cost and performance Build and maintain Microsoft Fabric artefacts including lakehouses, warehouses, notebooks, dataflows, and semantic models Perform advanced data transformations using SQL, Python, Spark, or Fabric Data Engineering experiences to prepare data for analytics and reporting Manage and optimise data models used by Power BI, ensuring good performance, relationships, quality, and reusability Support enterprise BI development by preparing datasets, semantic models, and reusable components within Power BI and Fabric Apply data governance and security best practices by implementing Purview classification, lineage, access controls, and policies Conduct data quality checks, data validation routines, and issue resolution to ensure trustworthy and reliable datasets Work closely with cross-functional teams (data architects, analysts, data scientists, product teams) to understand requirements and translate them into robust data solutions Engage directly with clients and stakeholders to clarify data needs, communicate progress, and explain technical concepts in a clear, accessible way Take ownership of more complex engineering tasks and assist senior engineers with solution components requiring deeper technical insight Mentor junior engineers by providing guidance, performing code reviews, and sharing best practices in engineering standards and Azure environments Contribute to CI/CD processes, version control practices, and automated deployment pipelines for data solutions Support the creation of documentation for pipelines, datasets, data models, and solution designs Troubleshoot pipeline failures, performance issues, and data inconsistencies, ensuring the stability and reliability of production workloads Stay current with emerging tools and capabilities across Azure, Microsoft Fabric, Power BI, Purview, and Microsoft Foundry Identify opportunities to improve efficiency, automation, scalability, or maintainability across existing pipelines and solutions Contribute to internal knowledge sharing, best-practice development, and engineering accelerators or templates