Business Intelligence Engineer, SQL, Tableau

Overview We are seeking an experienced Business Intelligence Engineer with strong AWS Data Engineering capabilities to support the development of a modern data platform while contributing to emerging AI-driven initiatives. This role is ideal for a hands-on engineer who combines Business Intelligence expertise with AWS data engineering experience and has practical exposure to AI/ML technologies, tools, or initiatives. You will play a key role in enhancing reporting and analytics capabilities, supporting existing data services, and helping shape future data and AI solutions. Key Responsibilities Design, develop, maintain, and optimise AWS-based data pipelines and platform components. Develop and support Power BI reports, dashboards, and analytical solutions across multiple business functions. Build and maintain robust data models and semantic layers within Power BI. Contribute to the development and enhancement of a modern data lake and data platform architecture. Support both BAU activities and the delivery of new data and analytics capabilities. Collaborate with stakeholders to gather requirements, define user stories, and translate business needs into technical solutions. Work closely with technical and business teams to support AI-related initiatives and roadmap delivery. Ensure best practices are followed for data quality, governance, security, and performance optimisation. Manage Power BI administration activities including access management, role-based security, subscriptions, and gateway configuration. Support migration of reporting and data workloads from legacy platforms into modern AWS and Power BI environments. Maintain code repositories and contribute to CI/CD and DataOps practices where applicable. Core Technical Requirements Strong hands-on experience with: Power BI report and dashboard development. Power BI administration, including security, access management, gateways, and subscriptions. Advanced SQL development and optimisation. AWS services including Athena, S3, Lambda, Glue, Redshift, EC2, and RDS. PostgreSQL, Postgres RDS, MySQL, and AWS Athena. Data modelling and semantic layer design. DAX query language. Python for data processing and pipeline development. GitHub and source code management. Agile delivery environments. AI / ML Exposure (Essential) You must demonstrate practical exposure to one or more of the following: Working with cloud-based AI services such as AWS Bedrock or similar platforms. Supporting AI-enabled products, workflows, or data solutions. Exposure to Generative AI technologies and prompt engineering concepts. Experience working alongside Data Science teams or supporting ML pipelines. Understanding of how AI capabilities can be integrated into data platforms and analytics solutions. Please note that AI/ML does not need to be your primary specialism; however, practical exposure must be clearly demonstrated. Experience Required Typically 4–8+ years of experience in Business Intelligence, Data Engineering, Analytics Engineering, or related disciplines. Proven track record delivering AWS-based data and analytics solutions. Strong experience developing Power BI reporting solutions in enterprise environments. Experience working with data warehouses, analytics platforms, and modern data architectures. Experience supporting and enhancing production reporting environments. Ability to engage effectively with technical and non-technical stakeholders. Desirable Skills AWS Bedrock or other cloud AI services. Data lake implementation experience. Infrastructure as Code (Terraform). API integration experience. CI/CD and DataOps practices. Experience migrating reporting platforms and legacy data solutions. Experience working within complex, regulated, or public sector environments. Role Split Approximately 50% Business Intelligence and Data Engineering delivery/support. Approximately 50% New capability development including data platform enhancement and AI initiatives. Key Attributes Strong problem-solving and analytical mindset. Hands-on engineering approach with attention to detail. Comfortable working within evolving and fast-paced environments. Ability to bridge the gap between data engineering, analytics, and emerging AI use cases. Proactive, collaborative, and delivery-focused. Excellent communication and stakeholder engagement skills. Able to work independently while contributing effectively within cross-functional teams. Qualifications Essential ~ Degree in Computing, Engineering, Data Science, Mathematics, or another numerate discipline.