About Us

At ANZ, we're shaping a world where people and communities thrive, driven by a common goal: to improve the financial wellbeing and sustainability of our millions of customers.

About the Role

We’re seeking a talented Data Engineer with a strong interest in Machine Learning to join the Australia Data tribe within our Retail division. In this role, you'll design and implement data processing solutions that support analytics, reporting, data development, and ML model integration across the division. You will drive best practice data engineering, challenge inefficient practices, and influence change to improve business outcomes. Your work will focus on delivering end-to-end solutions, uplifting the data engineering and ML engineering capability within the tribe, and aligning with the broader Enterprise Analytics roadmap. 

What will your day look like?

As a Data Engineer, you will:
 

• Design data structures for data ingestion, integration, and analytics layers, supporting both traditional data engineering workflows and machine learning model pipelines.
• Work in a cross-skilled squad with people both from business and technology to build end-to-end features, including machine learning model deployment and monitoring.
• Contribute to design patterns for both on-premises and cloud-based environments, ensuring scalability and efficiency for data and machine learning workflows.
• Optimize data flows by building robust, fault-tolerant data pipelines that clean, transform, and aggregate unorganized and messy data into databases or data sources, with an emphasis on preparing data for ML models.
• Collaborate with ML engineers and data scientists to integrate machine learning models into production systems, ensuring smooth data flow between feature engineering, model training, and model deployment.
• Implement automated testing, monitoring, and validation of data pipelines and ML models in production to ensure continuous delivery and model performance. 

What will you bring?

• Data Engineering Languages – Applies advanced language features and third-party libraries (Python, SQL, Spark), refactors code for maintainability, and manages complex concerns like concurrency and performance. Familiarity with libraries like scikit-learn, TensorFlow, or PyTorch is a plus.
• Data Flow Engineering – Develops data pipelines and schemas, turning designs into implementations, and identifies potential issues or complexities. Experience with ML model pipelines is highly valued.
• Machine Learning Integration – Works with data scientists to integrate machine learning models into production systems. Knowledge of ML model lifecycle management, including training, validation, and deployment, is highly desirable.
• Data Quality Assurance – Implements minor changes to existing pipelines and follows detailed design instructions, ensuring high-quality data is prepared for both analytics and ML models.
• Documentation – Creates clear, organized, and accurate documentation independently, with attention to detail, including data pipeline and ML model integration documentation.
• Soft Skills – Communication, problem-solving, planning, prioritization, and critical thinking are essential in collaborating with both technical and non-technical stakeholders.
• Incident, Risk, and Issue Management – Understands data security policies and incident management basics, applying them with support to minimize risks and follow predefined workflows.
• Change Management – Understands basic change management principles and follows structured processes under guidance, especially in the context of deploying data and ML solutions.
• Agile Practices, Data Privacy & Ethics – Understands and applies basic Agile principles, data privacy, and ethics with guidance, contributing to team workflows while adhering to supervision.
• Machine Learning Exposure – A basic understanding of machine learning concepts and their application in a production environment is beneficial (e.g., model training, model testing, model deployment, model monitoring). 
• Industry Experience: 7-8 years of experience as DE/MLE. 

You’re not expected to have 100% of these skills. At ANZ, a growth mindset is at the heart of our culture, so if you have most of these things in your toolbox, we’d love to hear from you. 

Job Posting End Date

9th May 2025, 11.59pm, (Melbourne Australia)