Chapter Lead - Data Engineering (Economic Crime)

About the role

CommBank is looking for a Senior Engineering Manager / Chapter Lead (Data Engineering) to lead a specialist data engineering chapter within its Economic Crime function. The role splits between 50% hands-on engineering (solution design, architecture, coding, platform uplift, troubleshooting) and 50% people and capability leadership (coaching engineers, setting standards, managing performance). It sits in Financial Crime Compliance, which governs anti-money laundering, counter-terrorism financing, sanctions, anti-bribery and corruption, and anti-tax evasion risks.

Key responsibilities

• Design solutions, architectures and code, review technical work, lift the platform and troubleshoot.
• Lead, coach and develop data engineers across several squads, supporting wellbeing, capability and performance.
• Guide the design, delivery, testing and maintenance of trusted data products, pipelines, reporting solutions, dashboards and analytical datasets.
• Drive uplift toward full-stack data engineering, reducing handoffs across requirements, build, testing, controls and operations.
• Partner with the Crew Tech Lead, Financial Crime Compliance, Risk, Operations, Technology, Data Governance and business teams to align delivery with strategy, KPIs and OKRs.
• Strengthen data quality, lineage, reconciliation, monitoring, observability and reporting controls.
• Modernise financial crime technology, data and reporting, and champion DevSecOps, platform health, vulnerability reduction and secure, scalable engineering.
• Support the move from legacy big data and warehouse technologies to cloud, AWS data services, Snowflake and AI-enabled ways of working.

Required profile

• Proven experience in solution design, architecture, coding, technical reviews, platform uplift and troubleshooting.
• Experience leading, coaching and developing data engineering teams.
• Strong experience with AWS data services and modern cloud data engineering practices.
• Experience designing and leading large-scale data lakes, data warehouses, data pipelines or analytics platforms.
• Strong understanding of data quality, lineage, reconciliation, control frameworks, assurance and auditability.
• Experience in complex enterprise, financial services, risk, compliance or regulated environments.

Required skills

• Knowledge of DevSecOps, observability, vulnerability reduction, automation and technology OKRs.
• Exposure to big data and data platform technologies such as Spark, Hadoop, Hive, Teradata, SQL, PL/SQL, Snowflake, AWS Glue or similar.
• Interest or experience in AI, ML and AI-powered engineering is highly regarded.