Top Data Engineer Jobs in Melbourne

The Principal Data Engineer will design and deliver scalable data architectures, optimize data pipelines, and guide clients in data management strategies. Responsibilities include technical leadership, collaborating with teams, and translating complex concepts for diverse audiences.

Lead design and delivery of scalable data and AI platforms across Azure and Databricks. Architect production-grade ML/LLM solutions (RAG, agentic patterns), build batch and streaming pipelines, formalize medallion/lakehouse standards, implement MLOps/DataOps (CI/CD, monitoring, orchestration), embed Responsible AI and governance, and provide technical leadership, mentoring, and cross-functional delivery.

The Principal Data Engineer will architect cloud-native platforms, oversee model execution and data management while ensuring compliance with regulatory standards in a high-performing team.

The Data Engineer will design, build, and manage scalable data pipelines on GCP, ensuring data quality and operational excellence while collaborating with data teams.

Design and deliver scalable multi-cloud data solutions using Databricks/Snowflake and modern data stacks. Build ingestion, transformation, modelling and access layers, implement dbt and CI/CD, ensure data quality, governance and lineage, support analytics and AI enablement, and consult directly with clients while mentoring junior engineers.

The Software Developer - Data Engineer will build ETL packages, develop scripts, and automate processes while ensuring data security in cloud environments.

The Data Engineer will build and manage scalable data pipelines and databases, collaborate on architecture, and drive data systems towards real-time capabilities in a fast-paced, innovative environment.

In this role, a Data Engineer will develop and maintain data pipelines, implement quality checks, enhance reporting processes, and collaborate with stakeholders.

Kogan.com seeks a Data & ML Engineer to develop data pipelines, support ML workflows, and ensure data integrity for eCommerce operations.

Design and implement scalable data pipelines and data warehouse solutions, transforming data for Snowflake and ensuring data quality and integrity while optimizing performance.

Build and maintain a scalable Azure-based data platform (ADF, Databricks, Synapse, Azure SQL). Design batch and streaming pipelines, implement lakehouse/Delta Lake architectures, enable CI/CD and IaC, monitor performance/cost, support AI/ML workloads, ensure security and governance, and partner with analytics and AI teams to deliver trusted data for insights.

Design and build scalable, secure data platform infrastructure on Databricks and Azure. Implement medallion ETL/ELT pipelines, multi-tenant isolation, graph data integrations, RBAC and privacy controls, CI/CD and observability. Lead architecture, mentor engineers, and align platform capabilities with analytics, BI, and AI teams.

Senior Data Engineer responsible for designing, building and operating scalable cloud data platforms using Scala/Java, Spark and AWS. Deliver ETL pipelines, data modelling, pipeline orchestration, performance tuning, automated testing, and secure data controls. Engage stakeholders, produce technical designs and documentation, mentor engineers, and provide staff-like ownership on initiatives within Financial Crimes Intelligence.

As a Data Engineer, you will build and maintain data systems for drug safety AI tools, ensuring reliable data processing and storage for research teams.

As a Lead Data Engineer, you'll design and maintain platform capabilities, CI/CD pipelines, and cloud infrastructure, ensuring system reliability and developer productivity while utilizing AI tools effectively.

Seeking a Senior Data Engineer to design and deliver data solutions in a hybrid work environment, focusing on large-scale projects and collaboration with teams.

This role involves sourcing audio data, managing cloud infrastructure, collaborating on data processing workflows, and contributing to dataset roadmaps.

The Senior Data Engineer will lead the design and delivery of scalable data platforms on Azure or AWS, manage data pipelines, and provide technical leadership in a consulting environment.

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 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.

Build and maintain a near real-time financial dashboard by integrating market and forecast APIs, automating financial models, and crafting clear visual narratives using Flourish and Canva. Collaborate with design and marketing partners, ensure data accuracy and performance, and optimize data flows across multiple feeds.

Operate, monitor and maintain the MPLS core, data centre and global WAN; configure and deploy network infrastructure; troubleshoot complex underlay/overlay issues; develop automation and CI/CD tools; respond to incidents and participate in on-call; produce documentation and support project delivery.

As a Senior Data Engineer, you'll design and build scalable data pipelines, maintain data warehouse solutions primarily using Snowflake, and implement data transformation workflows with dbt. You'll also ensure data quality and collaborate with a cross-functional team.

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.

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.