Business Intelligence Engineer (AWS/AI Exposure)

We’re looking for an experienced Business Intelligence Engineer/AWS Data Engineer to sup-port the development of a modern data platform and contribute to emerging AI-driven initiatives. This role will suit someone with a strong foundation in AWS data engineering and BI, alongside practical exposure to AI/ML concepts or tools.

Key Responsibilities: Design, build and optimise data pipelines and data platform components within AWS Support and enhance Business Intelligence reporting and analytics capabilities Contribute to the development of a data lake and modern data architecture Work across both BAU support and new capability development Collaborate with wider teams on AI-related initiatives and roadmap delivery Apply best practices around data quality, governance and performance optimisation

Core Technical Requirements: Strong hands-on experience with: AWS Data Engineering stack (eg Glue, S3, Lambda, Redshift, Athena) SQL (advanced level) Python (for data processing and pipeline development) Infrastructure/tooling exposure (eg Terraform, APIs, CI/CD beneficial) Experience working in Business Intelligence/analytics environments

AI/ML Exposure (Key Requirement): Candidates must demonstrate some level of exposure to AI/ML, such as: Working with cloud-based AI services (eg AWS Bedrock or similar) Supporting AI-enabled data products or workflows Understanding of generative AI/prompt engineering concepts Exposure to ML pipelines or collaborating with Data Science teams This does not need to be a core specialism but must be clearly evidenced and practical.

Experience Required: Typically 4-8+ years’ experience in data engineering/BI roles Proven experience delivering AWS-based data solutions Background in data warehousing, analytics, or data platform development Experience working in complex or regulated environments is beneficial

Desirable: Knowledge of AWS AI services (eg Bedrock) Experience contributing to data lake builds or modern data platforms Exposure to DataOps/CI-CD practices Public sector experience (nice to have)

Role Split: ~50% Business Intelligence/Data Engineering delivery & support ~50% New capability development, including data platform and AI initiatives

Key Attributes: Strong problem solver with a hands-on engineering mindset Comfortable working in a developing/evolving environment Able to bridge the gap between data engineering and emerging AI use cases Proactive and collaborative approach

Summary: This is an opportunity to join an organisation investing heavily in its data platform and future AI capabilities, where you’ll play a key role in shaping both current BI delivery and next-generation data solutions

Strong hands-on experience with: AWS Data Engineering stack (eg Glue, S3, Lambda, Redshift, Athena) SQL (advanced level) Python (for data processing and pipeline development) Infrastructure/tooling exposure (eg Terraform, APIs, CI/CD beneficial) Experience working in Business Intelligence/analytics environments

AI/ML Exposure (Key Requirement): Candidates must demonstrate some level of exposure to AI/ML, such as: Working with cloud-based AI services (eg AWS Bedrock or similar) Supporting AI-enabled data products or workflows Understanding of generative AI/prompt engineering concepts Exposure to ML pipelines or collaborating with Data Science teams This does not need to be a core specialism but must be clearly evidenced and practical.

Experience Required: Typically 4-8+ years’ experience in data engineering/BI roles Proven experience delivering AWS-based data solutions Background in data warehousing, analytics, or data platform development Experience working in complex or regulated environments is beneficial

Desirable: Knowledge of AWS AI services (eg Bedrock) Experience contributing to data lake builds or modern data platforms Exposure to DataOps/CI-CD practices Public sector experience (nice to have)

Role Split: ~50% Business Intelligence/Data Engineering delivery & support ~50% New capability development, including data platform and AI initiatives

Key Attributes: Strong problem solver with a hands-on engineering mindset Comfortable working in a developing/evolving environment Able to bridge the gap between data engineering and emerging AI use cases Proactive and collaborative approach