Specific work or activities you will perform:
Lead the design and build of the enterprise data platform with a strong product mindset, focusing on scalable, real-time and batch data capabilities. Drive the architecture and implementation of streaming data platforms (e.g., event-driven systems, real-time ingestion and processing) to enable low-latency data use cases across the enterprise.
Provide technical leadership across data platform engineering, ensuring robust design patterns, platform reliability, and engineering excellence at scale. Champion modern engineering practices including CI/CD, platform automation, observability, and resilience.
Establish and role-model the right engineering culture, hands-on leadership, ownership, high standards. Collaborate closely with product, architecture, and domain teams to evolve platform capabilities as reusable products that accelerate data and AI initiatives across the organisation.
Business outcome this work enables:
Enables a scalable, real-time capable enterprise data platform that supports mission-critical workloads, including streaming analytics, AI/ML use cases, and data-driven decisioning. Improves time-to-market for data products through a product-led platform approach, reducing duplication and increasing reuse.
Strengthens engineering quality, platform resilience, and operational efficiency through standardised practices and strong technical leadership. Supports enterprise-wide data modernisation by providing a robust, governed, and high-performance platform foundation aligned to future growth.
About the Role:
As a Chief Engineer for Data and analytics, you’ll play a vital role in designing, building, and maintaining the infrastructure and tools that drive our analytics teams to deliver business value at scale. Your expertise in the AWS ecosystem will help automate and streamline the data and analytics platform , ensuring our platforms deliver quality, agility, and security for customer-facing products. You’ll collaborate closely with product engineers and data scientists to provide reliable, seamless machine learning solutions.
What You’ll Do:
Design, implement, and maintain scalable and reliable data and analytics infrastructure on AWS + Snowflake.
Develop and support tools for the end-to-end data lifecycle, including data ingestion, model deployment, and monitoring, with an emphasis on automation and DevSecOps.
Work collaboratively with data scientists, engineers, and stakeholders to translate requirements into robust technical solutions.
Build and maintain CI/CD pipelines to streamline ML model deployment and updates.
Develop and maintain thorough platform documentation, including architecture blueprints and best practices.
What We’re Looking For:
Hands-on experience with AWS services such as EC2, ECS, S3, Lambda, Step Functions, RDS, DynamoDB, IAM, VPC, Route 53, and CloudWatch.
Experience with AWS & Snowflake services such as SageMaker, AWS Glue, Amazon EMR, and Airflow.
Experience in building and scaling cloud infrastructure for machine learning use cases.
Solid understanding of the ML lifecycle: data preprocessing, feature engineering, model training, evaluation, and deployment.
Proficient scripting and automation skills in Bash and Python.
Experience with Infrastructure-as-Code tools, especially AWS CloudFormation.
Proficiency with version control systems such as GitHub and GitHub Actions.
Familiarity with monitoring and observability tools like Grafana and Prometheus, as well as engineering tools such as Artifactory, Snyk, and Docker.
Comfortable working across the full software lifecycle: design, build, change, and run.
Strong communication skills and a commitment to inclusivity and continuous improvement.
If you're already part of the Commonwealth Bank Group (including Bankwest, x15ventures), you'll need to apply through Sidekick to submit a valid application. We’re keen to support you with the next step in your career.
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Skills Required
- Hands-on experience with AWS services (EC2, ECS, S3, Lambda, Step Functions, RDS, DynamoDB, IAM, VPC, Route 53, CloudWatch)
- Experience with Snowflake
- Experience with SageMaker, AWS Glue, Amazon EMR, and Airflow
- Experience building and scaling cloud infrastructure for machine learning use cases
- Solid understanding of the ML lifecycle (data preprocessing, feature engineering, model training, evaluation, deployment)
- Proficient scripting and automation skills in Bash and Python
- Experience with Infrastructure-as-Code tools, especially AWS CloudFormation
- Proficiency with version control systems such as GitHub and GitHub Actions
- Familiarity with monitoring and observability tools like Grafana and Prometheus
- Familiarity with engineering tools such as Artifactory, Snyk, and Docker
- Experience with CI/CD, platform automation, observability, and resilience practices
- Strong communication skills and commitment to inclusivity and continuous improvement
- Hands-on leadership and ability to establish engineering culture and ownership
What We Do
Australia’s leading provider of financial services including retail, premium, business and institutional banking, funds management, superannuation, insurance, investment and sharebroking products and services. We are a business with more than 800,000 shareholders and over 52,000 employees. We offer a full range of financial services to help all Australians build and manage their finances.
