Staff Data Engineer

Commonwealth Bank; City of Melbourne; On-site; AUD 120,000 - 170,000; Full time

Job summary

Commonwealth Bank is seeking a Staff Data Engineer in Melbourne to design and deliver scalable, secure data solutions across pipelines, ontologies and data products. You will automate development, deployment, testing and ongoing management of dashboards and analytics.

You will mentor engineers, implement data quality and governance, and lead complex data projects leveraging Python and SQL with cloud platforms and MLOps awareness.

Qualifications

• Strong experience in data engineering and large-scale data platforms.
• Advanced Python and/or R programming with strong SQL capability.
• Experience building production-grade batch or streaming pipelines.
• Experience automating data and analytical product lifecycles.
• Experience with data modeling, ontologies or semantic data layers.
• Exposure to MLOps and productionisation of ML models.

Responsibilities

• Design and build scalable batch, streaming and analytical data pipelines.
• Transform data into trusted, reusable datasets for dashboards.
• Create curated data layers and reusable data products for analytics.
• Automate the dashboard data lifecycle, including testing and deployment.
• Build CI/CD pipelines using GitHub Actions.
• Design data models, ontologies and common data definitions.
• Implement data matching, entity resolution, and data cleansing.
• Ensure data solutions meet security, privacy, resilience and governance.
• Lead engineering standards and mentor engineers.

Skills

Data engineering

Python

SQL

CI/CD

GitHub Actions

Big data

MLOps

Data modelling

Cloud platforms

Mentoring

Security & governance

Automation

Entity resolution

Tools

GitHub Actions

Job description

Role Purpose The Staff Data Engineer is a senior technical contributor responsible for designing and delivering scalable, reliable and secure data solutions. The role provides technical leadership across data engineering, analytical pipelines, data ontology, data matching, data cleansing, MLOps and automation. A key focus is transforming complex source data into trusted, reusable and dashboard-ready data products, while automating their development, deployment, testing and ongoing management.

Key Responsibilities:

• Design and build scalable batch, streaming and analytical data pipelines.
• Transform source data into trusted, reusable and analysis-ready datasets.
• Create curated data layers, semantic models and reusable data products for dashboards, reporting and analytics.
• Automate the dashboard data lifecycle, including ingestion, transformation, testing, deployment, monitoring, refresh and issue remediation.
• Design and implement agentic workflows that coordinate data preparation, quality validation, metadata generation, dashboard updates and operational support.
• Build and maintain CI/CD pipelines using GitHub Actions.
• Design data models, ontologies, taxonomies and common data definitions.
• Implement data matching, entity resolution, deduplication and data cleansing solutions.
• Build automated data quality, validation, reconciliation and observability controls.
• Develop solutions for large and complex datasets using big data and distributed processing technologies.
• Support the productionisation and monitoring of machine learning and open-weight models.
• Develop production-grade solutions using Python and/or R.
• Establish engineering standards, mentor engineers and provide technical guidance.
• Ensure data solutions meet security, privacy, resilience and governance requirements.

• Strong experience in data engineering and large-scale data platforms.
• Strong algorithmic optimisation capabilities
• Advanced Python and/or R programming skills, with strong SQL capability.
• Experience building production-grade analytical, batch or streaming pipelines.
• Experience preparing reusable datasets and semantic data layers for dashboards, reporting and analytics.
• Strong experience implementing CI/CD pipelines using GitHub Actions.
• Experience automating data and analytical product lifecycles, including testing, deployment, monitoring and release management.
• Experience designing or implementing agentic workflows, AI-driven automation or intelligent orchestration.
• Experience with big data and distributed processing technologies.
• Experience with data modelling, semantic modelling or data ontology.
• Experience with data matching, entity resolution, deduplication and data cleansing.
• Experience implementing data quality and data observability controls.
• Exposure to MLOps and the productionisation of analytical or machine learning models.
• Strong cloud platform, software engineering, automated testing and infrastructure-as-code experience.
• Demonstrated technical leadership and mentoring capability.

• Experience evaluating, fine-tuning, deploying or operating open-weight models.
• Exposure to retrieval-augmented generation, embeddings, vector databases or model-serving technologies.
• Experience applying AI agents to data engineering, analytics or dashboard operations.
• Exposure to cyber security controls and secure data engineering.
• Exposure to identity and access management, identity governance or identity-related datasets.
• Experience in regulated or risk-sensitive environments.

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Staff Data Engineer • Melbourne, VIC, AU