Business Intelligence Engineer

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.