Top Data Engineer Jobs in Melbourne

Build and maintain SQL data models, dbt pipelines, governed Snowflake semantic layers, and data products supporting Finance, People Experience, and Legal. Ensure data quality, testing, documentation, and reliable reporting for executive and market-facing insights. Collaborate with business teams to translate questions into trusted metrics, contribute to conversational AI and agentic data capabilities, mentor teammates, and establish consistent engineering standards.

Own and improve the reliability, quality, governance, and observability of the company’s internal data platform. Build production data pipelines and integrations, implement testing and validation, document lineage, enable self-service analytics, and automate operational support. Collaborate with product engineers, delivery teams, and data specialists while using AI-assisted development tools to improve data engineering practices.

Architect and implement scalable, reliable data platform solutions, establish architectural standards, evaluate emerging technologies, and improve data integration and processing. Collaborate with integration and data architects, stakeholders, and multiple teams to align designs with business and technical requirements. Provide technical leadership, support team members, drive platform performance improvements, and serve as a subject matter expert.

Own and evolve major areas of the data platform, ensuring reliability, quality, scalability, and cost efficiency. Lead Snowflake performance optimization, dbt modeling standards, pipeline architecture, observability, incident resolution, and governance. Partner with product, commercial, and analytics stakeholders to translate requirements into solutions. Mentor engineers, improve team capability, and evaluate responsible AI tooling for data engineering workflows.

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.

Architect and implement scalable data platform capabilities and enterprise MDM features (identity resolution, golden records, matching). Build APIs, batch/streaming pipelines, validation, lineage, observability, and CI/CD. Use AI-assisted development and build AI-enabled data quality and automation features. Mentor engineers and lead cross-team technical direction.

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.

Leads technology strategy, architecture, and delivery for scalable CRM, customer engagement, workflow, and AI-enabled platforms supporting Business Banking. Drives cloud-native, API-driven, distributed-system engineering practices across multiple teams; establishes reusable standards, DevSecOps, observability, resilience, and responsible AI controls. Partners with senior stakeholders, mentors engineers, and influences platform strategy and technology investment.

Designs and administers secure, governed Google Cloud environments, focusing on IAM, RBAC, BigQuery permissions, policy tags, masking, service accounts, and least-privilege access. The role develops Terraform-based infrastructure, troubleshoots configuration issues, supports dbt and data-platform deployments, migrates governed data-access patterns, documents solutions, and guides engineers and stakeholders through cloud configuration and access challenges.

Design, build and optimise cloud-native, enterprise-scale data platforms and pipelines using Snowflake and AWS. Deliver trusted analytics-ready datasets, improve performance/resilience, drive automation and engineering best practices, support production systems, and mentor colleagues to enable analytics, AI, fraud detection and customer intelligence.

Design and deliver scalable batch, streaming and analytical data pipelines and reusable semantic data products. Automate data product lifecycles, implement data matching, cleansing, quality and observability controls, support ML productionisation, build CI/CD with GitHub Actions, and provide technical leadership and governance for secure, resilient data solutions.

Lead the design and development of cloud-based data platforms, pipelines, ETL/ELT processes, data models, warehouses, and lakes using Azure and Databricks. Build integrations, optimize performance, implement governance, monitor pipelines, resolve issues, and create Power BI reports. Collaborate with product managers and technical teams to deliver scalable, reusable data solutions and document processes.

Lead the design and development of cloud-based data platforms using Azure and Databricks. Build and optimize ETL/ELT pipelines, data models, warehouses, lakes, and integrations; implement governance, monitoring, and troubleshooting; and create Power BI reports. Collaborate with product managers and cross-functional teams to deliver scalable, high-quality data solutions, while documenting processes and applying coding and DevOps best practices.

Leads hands-on delivery and operations for scalable data platforms, pipelines, and data products. Translates architecture into secure solutions, enforces engineering and DevSecOps standards, supports development and production reliability, and leads incident resolution. Provides technical direction to distributed engineers, reviews designs and code, coaches team members, and advances platform interoperability, governance, observability, automation, and cloud transformation initiatives.

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.

Designs, builds, and optimizes cloud-native data platforms, batch and real-time pipelines, and AI-powered data products using Snowflake, Databricks, AWS, and Azure. The role includes data architecture, governance, quality, observability, security, performance and cost optimization, infrastructure as code, and direct client collaboration. The engineer also contributes to technical design, estimation, delivery planning, and modern AI capabilities such as semantic search and RAG.

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.

Designs, builds, and optimizes ETL/ELT pipelines connecting enterprise applications, databases, cloud platforms, warehouses, and analytics environments. Develops reusable integration frameworks, transformation and validation logic, monitoring, and data-quality processes. Supports migrations, implementations, upgrades, automation, testing, and CI/CD. Collaborates with architects, engineers, analysts, and business stakeholders to deliver secure, scalable, reliable data solutions.

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

Designs, develops, and maintains secure enterprise data pipelines and ETL/ELT processes integrating government and commercial systems. Ensures data quality, performance, availability, and real-time delivery for logistics, mission support, analytics, and operational decision-making. Responsibilities include database and pipeline optimization, cloud migration support, technical documentation, cybersecurity and governance compliance, troubleshooting, stakeholder coordination, operational exercise support, and mentoring junior personnel.

Migrate SQL Server data products and analytical workloads to a modern cloud platform. Redesign legacy SQL, ETL processes, and business logic; build scalable pipelines and analytical models; validate data quality; collaborate with stakeholders; and improve platform reliability, performance, cost efficiency, and AI-assisted data engineering automation.