Top Data Engineer Jobs in Melbourne

Design, build, and operate scalable data platform pipelines and models using Snowflake, dbt, Prefect, Flink and AWS. Own end-to-end platform components, apply IaC with Terraform, implement CI/CD and testing, manage production systems, monitoring and incident response, and improve data quality and self-service capabilities for downstream teams.

Design, build, and maintain data pipelines and core customer datasets to consolidate legacy systems into a unified customer profile. Collaborate with data engineers, analysts, and cross-functional squads to ensure data accuracy, remediation, and enable personalization and ML capabilities. Work in a small, dynamic team operating within a hybrid Melbourne-based environment.

Hands-on data engineer working with clients to re-engineer, optimise and domain-align data models and pipelines using Snowflake and dbt. Contribute to solution design, performance tuning, CI/CD, testing, documentation, and collaborate within consulting delivery teams to ensure maintainability and lineage.

Lead architecture and delivery of scalable data solutions across squads. Design and optimise ETL/ELT workflows, mentor engineers, collaborate with data scientists and stakeholders, drive data modelling, orchestration, governance and strategic roadmap for the data platform.

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

Build and maintain ETL pipelines for large volumes of imagery and video data. Perform exploratory data analysis, clean and structure datasets, optimize SQL queries, create prototype visualizations, and ensure data is accurately labeled and ready for machine learning. Collaborate with ML engineers and document datasets, pipelines, and data-quality processes.

Design, build, and maintain scalable data pipelines and Delta tables using Microsoft Fabric and Python/Spark SQL. Optimize datasets for Power BI, monitor data quality, support CI/CD and deployments, manage Fabric performance and security, and document operational runbooks.

As a Data Engineer, you will develop and manage data pipelines, ensuring reliable data ingestion and delivery while optimizing for simplicity and performance.

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.

Design, build and optimise cloud data platforms and lakehouse solutions on Microsoft Azure and Microsoft Fabric. Develop scalable ETL/ELT pipelines, data models, and transformations using SQL, Spark and Python. Implement data governance, security, CI/CD and collaborate with stakeholders to enable analytics, reporting and AI use cases.

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

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.

Design, build, and maintain scalable, fault-tolerant GCP data pipelines and BigQuery data models. Collaborate with data scientists and engineers to ensure data quality, monitor production systems, implement CI/CD for pipelines, and recommend cloud data architecture and governance improvements.

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.

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

Designs and builds GYG’s enterprise data platform, including Microsoft Fabric ingestion pipelines, governed medallion architecture, cloud lakehouse migration, data integrations, Power BI semantic models, DAX solutions, data quality controls, lineage, and AI-ready data products. The role owns end-to-end solution design, establishes engineering standards, ensures pipeline reliability, and challenges unsuitable data requirements.

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.

Lead design and build scalable data platforms and ELT pipelines using Databricks or Snowflake and dbt. Design architecture across ingestion, storage, modelling and consumption layers, optimise performance, configure platform components, implement CI/CD, automate testing and monitoring, manage vendor relationships, support data quality and lineage, and mentor junior engineers while enforcing engineering best practices.

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.

Lead design and delivery of scalable, cloud-native software and data/AI-enabled platforms for Business Banking. Build APIs, services and platform capabilities, drive architecture and engineering standards, mentor engineers, champion CI/CD, observability and DevSecOps, and support production platforms to deliver customer and colleague digital experiences.

Lead architecture and delivery of AI-enabled CRM, customer engagement and banker experience platforms. Drive platform strategy across cloud, APIs, workflow orchestration and AI, establish engineering standards, ensure security/privacy/responsible AI, mentor engineers, and influence stakeholders to deliver scalable, resilient customer and colleague experiences.

Design and maintain StratFin data models, automate reporting and data workflows, build reliable pipelines with testing and monitoring, partner with Finance and Strategy to deliver scalable data products, and establish data quality standards to enable analytics and AI.

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.