Senior Data Engineer

• Translate business requirements into technical designs, delivery estimates, implementation tasks and acceptance criteria. 
• Design, build and support Microsoft Fabric solutions using appropriate lakehouse, warehouse, orchestration and semantic-modelling patterns. 
• Develop robust ingestion, ETL/ELT and integration pipelines using SQL, Python or PySpark as appropriate. 
• Design and maintain dimensional and analytical data models, including star schemas, facts, dimensions and Power BI semantic models. 
• Implement automated data-quality, validation, reconciliation and schema-change controls. 
• Contribute to architecture decisions and lead detailed design sessions for assigned components or workstreams. 
• Apply security and governance controls including least privilege, classification, lineage, metadata, retention and auditability. 
• Use source control, peer review, automated testing, CI/CD and repeatable deployment practices. 
• Create and maintain technical documentation including data flows, architecture views, configuration, support procedures and runbooks. 
• Estimate and plan engineering work, manage dependencies and communicate delivery risks within Agile teams. 
• Support testing, release, monitoring, troubleshooting, incident investigation and continuous improvement in production. 
• Mentor junior engineers, review technical work and promote reusable patterns and engineering standards. 
• Communicate solution options, progress, risks and recommendations to technical and non-technical stakeholders. 

Qualifications

Essential 

• Demonstrable hands-on experience delivering data engineering solutions using Microsoft Fabric. 
• Strong SQL development, troubleshooting and performance-optimisation skills. 
• Practical experience with Python or PySpark, notebooks and distributed data-processing concepts. 
• Experience building and operating ETL/ELT pipelines and integrating cloud, on-premises or third-party data sources. 
• Experience with Fabric lakehouse and warehouse patterns, orchestration, OneLake concepts and Power BI semantic models, including Direct Lake where appropriate. 
• Experience with data warehouses, data lakes and Azure data services such as Azure Data Lake Storage and Azure Data Factory. 
• Strong knowledge of dimensional modelling, analytical structures and data-modelling trade-offs. 
• Experience implementing unit, integration, reconciliation and regression testing for data solutions. 
• Experience with Git, pull requests, source control, CI/CD and automated deploymen