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Senior Data Analytics Engineer

Swgroup·Worldwide·Southampton, GB·senior
power bi
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At S&W, we help our clients thrive by simplifying the complex, illuminating new paths, and shaping solutions that make a difference. As one of the UK’s top 10 fastest-growing accountancy firms, we have been a trusted partner since 1881—helping businesses and individuals meet challenges and seize opportunities across generations. Built on expertise and driven by ambition, we provide a comprehensive range of services, including tax and accountancy, advisory and assurance, corporate finance, and restructuring. We are defined by our purpose—to help navigate challenges, unlock potential, and achieve the extraordinary. What will you be doing? We’re seeking a passionate and experienced Data Analytics Engineer to join a thriving team and help in the design, build and maintenance of scalable data solutions, that bring together data from across the organisation. You’ll ensure data is well structured, reliable and easy to use for reporting, analytics and in decision‑making. Working as part of the Data Platform Team, you’ll help create a high‑quality, future‑ready data environment that supports business needs now and we continue to grow! This is a permanent opportunity to be based at our Southampton office with office attendance expected to be 2 days per week. As a Data Analytics Engineer your responsibilities will include among others, Architect and maintain scalable ETL/ELT pipelines, optimising for performance, reliability, cost, and data quality across multiple data sources Maintain and troubleshoot medallion architecture and dimensional modelling (star schemas) to support enterprise reporting, KPIs, and Power BI semantic models Design, implement, and maintain Lakehouse and Warehouse structures in Microsoft Fabric, including data modelling standards and semantic layers Provide operational support, monitoring, troubleshooting, and performance tuning for Microsoft Fabric ingestion, dataflows, and transformation workloads Identify, resolve, and prevent data quality issues thro

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