Top Data Engineer Jobs in Melbourne

Design, deploy, and maintain AWS and Databricks data infrastructure, data lakes, and scalable ETL pipelines. Build custom ingestion services and connectors using Airbyte, Go, and Python; optimize performance, reliability, monitoring, and data quality. Implement cloud data governance, security, access controls, encryption, and compliance practices while collaborating with platform engineers, analysts, and stakeholders.

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.

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.

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

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

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.

Build and own Overflow's data infrastructure: design ETL/ELT and CDC pipelines, data warehouses, OLAP schemas, and a unified data layer. Enable self-service analytics and dashboards, implement Databricks Unity Catalog for governance, ensure data quality and monitoring, and partner with Head of AI and cross-functional teams to prepare semi-structured data for MLOps and advanced AI workflows.

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.

Build and improve backend and data pipelines to fetch and analyze smart meter time-series data, develop classical ML models to size batteries, prioritize features that drive revenue, and collaborate with founder, UX, and contractors to ship experiments rapidly.

Build and improve a platform that ingests smart-meter time series data, models household energy consumption, simulates battery performance, and prioritises features that drive revenue. Work across backend (Python/Clojure), frontend (TypeScript), time-series databases, and classical ML models to deliver production-ready analyses and experiments; collaborate with founder, UX specialist, and overseas developers; possibly manage a contractor.

Design, build and maintain data pipelines, models and APIs across ingestion, transformation and consumption. Lead technical design and architecture, resolve data quality and performance issues, support production with monitoring and automation, and mentor engineers. Work with Airflow/Breeze, BigQuery, Kafka, TypeScript/Node or Python, and cloud/DevOps tooling to deliver reliable, scalable property data platforms and APIs.

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.