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.

Design, build, and own cloud-based data pipelines and lakehouse infrastructure using Python, SQL, PySpark, dbt, and AWS. Ensure data quality, security, governance, compliance, performance, and reliability across production systems. Monitor and troubleshoot incidents, support API, streaming, and CDC ingestion, contribute to platform architecture, evaluate new technologies, mentor engineers, and lead technical projects in a regulated financial environment.

Develop and analyze enterprise data systems and data warehouse solutions in a banking environment. Responsibilities include expert SQL and PL/SQL development, data and SQL script analysis, Unix shell scripting, testing, scheduling with Control-M, CI/CD using Git and Jenkins, and deployment or management of data platforms. The role requires experience with Spark, Hadoop, Kafka, cloud technologies, AWS, programming in Python, Scala, or Java, and infrastructure-as-code and containerization tools.

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.

Designs and optimizes scalable data pipelines and architectures supporting analytics and operational needs. The role develops ETL, streaming, data quality, governance, and cloud-based workflows using modern big data technologies. Responsibilities include translating business requirements into technical solutions, monitoring data systems, improving reliability and performance, and providing technical guidance on data modeling, storage, and metadata management.

Implement cloud platform regression testing and automate large-scale transactional reconciliation using Python and SQL. Validate near-real-time data availability, data quality, transformations, and system stability across multiple data layers. Work with GCP services, data warehousing, ETL processes, CI/CD deployments, graph databases, and Cypher queries.

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.

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.

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.

Lead the design, development, testing, and deployment of scalable enterprise data pipelines and products supporting Home Buying. Provide technical leadership across architecture, governance, engineering standards, delivery coordination, and technical decision-making. Build solutions using AWS, Big Data technologies, Snowflake, and dbt; mentor engineers, support recruitment, collaborate with stakeholders, and contribute to GenAI adoption and data platform modernization.

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.