Design, build, and maintain scalable data engineering frameworks and platform utilities used across engineering teams Develop reusable patterns, templates, and abstractions to standardise and accelerate delivery Define and evolve platform architecture decisions, ensuring scalability, maintainability and consistency Design and implement CI/CD pipelines and automation frameworks to improve engineering velocity Define and enforce engineering standards for testing, code quality, deployment and documentation Identify and eliminate manual or repetitive processes through automation and tooling improvements Integrate AI-assisted development tools into engineering workflows to improve productivity Develop and maintain AI engineering assets such as coding guidelines, prompt frameworks and reusable agent configurations Lead the development and operational support of core data transformation frameworks (including dbt Core at enterprise scale) Investigate and resolve framework-level issues, including deployment failures, dependency conflicts and production incidents Support onboarding and enablement of engineering teams adopting platform toolin