Top Data Engineer Jobs in Melbourne

Lead the strategy, architecture, and engineering of AWS-based data and model platforms supporting credit risk and capital provisioning. Build scalable data pipelines, model deployment and orchestration frameworks, monitoring, governance, lineage, and auditability. Drive legacy platform migration, productionize ML workloads, ensure regulatory controls and data integrity, influence cross-team technical decisions, improve engineering standards, and mentor engineers.

Design and maintain scalable ETL/ELT pipelines for digital archival data. Automate cleansing, validation, standardization, enrichment, and transformation; develop schemas, ontologies, and data models; and integrate structured and semi-structured sources. Collaborate with archivists, product teams, developers, suppliers, and technical partners to solve data problems, communicate requirements, and improve data quality, discovery, reuse, and accessibility.

Lead modern Data & Analytics engagements from discovery through delivery while designing scalable cloud data solutions. Manage delivery teams, mentor consultants, advise senior client stakeholders, shape technical proposals, and contribute to consulting methodologies. The role requires hands-on expertise across data engineering, architecture, warehousing, integration, cloud platforms, dbt, ETL/ELT, BI, and technical project leadership.

Designs and delivers modern data and analytics solutions for clients, including data pipelines, models, databases, integrations, dashboards, and visualisations. The role covers discovery through implementation, testing, deployment, support, and client adoption. It requires direct stakeholder engagement, technical solution design, strong SQL and data engineering practices, project planning, and knowledge transfer across varied cloud and client environments.

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.

Partner with data domain and product owners to build trusted, AI-ready data products. Coach teams on data ownership, governance, lifecycle management, quality, observability, and data-as-a-product practices. Translate enterprise standards into practical delivery guidance, identify cross-domain opportunities, communicate data health and business value, and provide feedback to platform teams. The role combines hands-on data engineering with stakeholder management, capability uplift, and influence across a federated environment.

Develop and deploy AI, generative AI, machine learning, and data science solutions for consulting clients. Responsibilities include building RAG pipelines, AI agents, LLM applications, predictive models, deep learning systems, optimization solutions, and big-data applications. The role also involves model evaluation, AI observability, governance, research, and collaboration with architects, engineers, data scientists, and clients.

Implements cloud platform regression testing and automated validation for high-volume transactional data. Uses Python and SQL to reconcile data, verify near-real-time availability, and assess quality across multiple data layers. Works with GCP services, data warehouses, ETL transformations, CI/CD deployments, graph databases, and Cypher queries to ensure system stability and accurate outcomes.

Develop and maintain scalable Python applications, APIs, microservices, and data pipelines using PySpark and Airflow. Deploy and operate solutions on Azure with Docker and Kubernetes, implementing monitoring, logging, alerting, and production support. Collaborate with product managers, data scientists, and engineering teams on global projects while contributing to coding standards, architecture, testing, documentation, and Agile delivery.

Leads the design, development, deployment, and continuous improvement of scalable Azure data solutions. Builds cloud-native platforms using Spark, Databricks, Python, and SQL; establishes CI/CD, testing, automation, and DevOps practices; resolves complex technical issues; collaborates with global stakeholders; and mentors engineers while contributing to platform architecture and roadmap decisions.

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.

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 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 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.

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.

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.