As a Senior Data Analyst, you will:

• Analyse customer, quote, policy, claims, transaction and marketing data to identify trends, opportunities and risks.  
• Develop clear, decision-focused reporting and dashboards using Power BI.  
• Write efficient, maintainable SQL to transform and analyse data across warehouse and lakehouse environments.  
• Work with Microsoft Fabric, including Warehouses, Lakehouses, notebooks, pipelines and semantic models.  
• Build and support reporting and workflow automations using Python, Fabric and Power Automate.  
• Own analytical solutions from requirements gathering through development, validation, deployment, documentation and ongoing support.  
• Investigate anomalies, reconcile results across systems and improve data quality and reporting reliability.  
• Define and document business rules, calculations, data lineage and validation controls.  
• Balance recurring operational reporting, quick-turnaround analysis and longer-term data projects.  
• Partner with Marketing, Claims, Finance, Operations and Distribution to translate business questions into practical data solutions.  
• Communicate findings clearly, including the commercial impact, limitations and recommended actions.  
• Use AI-assisted development tools responsibly to improve productivity while maintaining appropriate review, validation and data security. 

As a Senior Data Analyst, you will:

• Analyse customer, quote, policy, claims, transaction and marketing data to identify trends, opportunities and risks.  
• Develop clear, decision-focused reporting and dashboards using Power BI.  
• Write efficient, maintainable SQL to transform and analyse data across warehouse and lakehouse environments.  
• Work with Microsoft Fabric, including Warehouses, Lakehouses, notebooks, pipelines and semantic models.  
• Build and support reporting and workflow automations using Python, Fabric and Power Automate.  
• Own analytical solutions from requirements gathering through development, validation, deployment, documentation and ongoing support.  
• Investigate anomalies, reconcile results across systems and improve data quality and reporting reliability.  
• Define and document business rules, calculations, data lineage and validation controls.  
• Balance recurring operational reporting, quick-turnaround analysis and longer-term data projects.  
• Partner with Marketing, Claims, Finance, Operations and Distribution to translate business questions into practical data solutions.  
• Communicate findings clearly, including the commercial impact, limitations and recommended actions.  
• Use AI-assisted development tools responsibly to improve productivity while maintaining appropriate review, validation and data security.