Top Data Engineer Jobs in Melbourne

Design, build, and maintain scalable, observable data pipelines and metadata frameworks to support analytics, AI/ML, and BI. Collaborate with engineers, data scientists, and analysts to improve data quality, governance, and delivery efficiency while driving best practices and MLOps integration.

Develop, deploy, and maintain LLM pipelines and RAG QA/search systems; design and optimize prompts and multi-agent LLM architectures; operate multi‑GPU/cluster inference; build evaluation pipelines for model quality, bias, and hallucination; collaborate with product and CS teams to integrate conversational AI.

Drive development of MYOB's SaaS platform as a senior full-stack developer. Collaborate on building web services and 3rd-party integrations, deploy cloud solutions (Azure/AWS) with scalability and resilience in mind, apply TDD, DevOps, CI/CD, clean code and event-driven architecture, and adapt to evolving technical directions.

Triage and resolve Master Data Management (Party, Risk, Reference) issues in Stibo STEP, run targeted data-cleansing sprints, monitor data quality dashboards, support releases/UAT, and maintain documentation and playbooks to prevent recurring data issues.

Lead design, development, deployment, and continuous improvement of cloud-native data and analytics solutions on Azure. Act as a hands-on technical lead building scalable, secure end-to-end data platforms using Spark/Databricks, Python/PySpark, and orchestration tools. Ensure CI/CD, DevOps, automation, code quality, mentor team members, resolve complex technical issues, and collaborate with stakeholders and vendors to drive platform evolution.

The Data & Analytics Engineer will integrate data stacks with products, build reports and dashboards, and enhance customer experience using AI-driven insights.

As a Senior Data Engineer, you will design and implement data transformation pipelines, gather business requirements, and work with various big data technologies.

As a Python and Kubernetes Software Engineer, you will develop data analytics solutions using open source tools, collaborating with a distributed team.

The Senior Data Engineer/Consultant will lead data engineering projects, ensuring high-quality technical outcomes using Databricks, while mentoring team members and collaborating with clients.

Lead design and delivery of modern cloud data platforms and scalable pipelines. Improve platform stability, implement data models and governance, support data product development, mentor junior consultants, and collaborate with stakeholders to translate business needs into reliable, automated data solutions.

Serve as the primary technical interface for data centre cooling, leading chilled water and liquid cooling solution design, technical pricing, and bid management. Influence early-stage design, ensure performance and resilience, develop cost models and value-engineered proposals, and align cross-functional stakeholders to win complex thermal infrastructure projects.

The Sr Data Engineer will optimize and refactor data pipelines, manage data ingestion from APIs, and support product engineering teams. Responsibilities include design best practices, enhance the modern data stack, and troubleshoot data issues, ensuring resilience and operational excellence.

Design and maintain scalable data pipelines and real-time streaming systems on AWS, enable ML/LLM-powered customer features, integrate Amazon Bedrock and models into agentic workflows, automate ML lifecycle steps, collaborate with ML, data science, and product teams to ship production-grade solutions.

Build and enhance enterprise analytics and visualization solutions: develop interactive dashboards, data-driven apps, transform and prepare data with Python, implement frontend visualization components with TypeScript, integrate with Microsoft and Google enterprise ecosystems, and use AI tools to accelerate development while collaborating with stakeholders and owning deliverables end-to-end.

The Senior Data Engineer will engineer and improve enterprise-scale data platforms on AWS, design and build end-to-end data pipelines, and optimize data processing solutions using Apache Spark and Python. Responsibilities include data governance, compliance controls, and collaboration with cross-functional teams to support analytics and data integration needs.