Manager, Risk Analytics Engineering

About the role

CommBank's Retail Risk Analytics, Insights & Visualisation team supports Retail Credit Risk through analytics, risk insights and data solutions. As Manager, Risk Analytics Engineering, you will be a hands-on technical leader who designs and delivers scalable data products and analytics engineering solutions, working across Risk, Business and Technology. The goal is to build modern ways of building, testing, deploying and managing analytical data products so analysts and risk teams can work more efficiently.

Key responsibilities

• Design, build and maintain scalable data pipelines, curated data products and transformation frameworks using advanced SQL, Python and modern analytics engineering tools.
• Establish reusable data models and engineering patterns that make trusted risk data easier to access and use across teams.
• Introduce automated testing, monitoring, documentation and data-quality controls across the analytical product lifecycle.
• Improve how analytical assets are developed and deployed through version control, CI/CD, orchestration and repeatable engineering practices.
• Contribute to cloud migration and platform modernisation, moving existing analytical processes toward scalable, maintainable solutions.
• Build automation and AI-assisted capabilities that improve productivity while meeting Risk governance and control expectations.
• Partner with risk specialists to turn portfolio questions and regulatory requirements into technical solutions and decision-ready insights.
• Provide technical leadership, coach colleagues and grow analytics engineering capability across the team.

Required profile

• Strong data or analytics engineering experience, comfortable staying close to the technology while influencing stakeholders.
• Experience designing and delivering production-quality data pipelines, transformation workflows or analytical data products.
• Ability to translate business, risk and regulatory requirements into data engineering solutions and clear insights.
• Demonstrated technical leadership, stakeholder engagement and coaching capability.
• A relevant qualification in data science, computer science, engineering, mathematics, statistics or a related discipline, or equivalent practical experience.
• Experience in credit risk, banking, financial services or another regulated environment is highly regarded.

Required skills

• Advanced SQL and strong practical Python.
• Analytical data modelling, automated testing, monitoring and data-quality controls.
• dbt or a comparable transformation framework.
• Cloud-based data platforms, version control, CI/CD and workflow orchestration.
• Dashboarding tools and exposure to AI-assisted tooling, development or machine-learning workflows are valuable.