Top Data Engineer Jobs in Melbourne

Design, build, and optimize scalable cloud data platforms, pipelines, data models, lakehouses, and warehouses using Microsoft Azure and Fabric. Integrate cloud and on-premises sources, develop transformations with SQL, Spark, and Python, and implement governance, security, quality, CI/CD, and performance controls. Collaborate with architects, analysts, data scientists, and stakeholders to support analytics, reporting, AI, and machine learning initiatives.

Designs, builds, and manages scalable, secure data products and platforms. Develops production-quality Python, PySpark, and SQL solutions; manages ingestion, orchestration, data models, CI/CD pipelines, and cloud data environments. Collaborates with project teams and stakeholders to ensure data warehouses meet business needs, while applying data governance, metadata, lineage, testing, documentation, and security standards.

Build and support scalable enterprise data solutions using Microsoft Fabric, including pipelines, lakehouses, warehouses, semantic models, and Power BI reporting. Responsibilities include data ingestion, transformation, modeling, governance, security, monitoring, troubleshooting, documentation, and platform modernization. Collaborate with stakeholders and technical teams to deliver reliable analytics solutions.

Build and maintain FinOps data pipelines, BigQuery and dbt models, cost attribution systems, forecasting and anomaly detection models, and cost-versus-revenue analytics. Identify and implement AWS and GCP savings opportunities, automate governance with Python, Terraform, and Lambda, and partner with Engineering, Finance, Product, and leadership on cloud costs, forecasting, reporting, and architecture decisions.

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.

Build and maintain scalable data pipelines, analytical datasets, semantic layers, and data models supporting reporting, analytics, AI, and business decision-making. Use Python, SQL, dbt, Airflow, and Snowflake to deliver reliable data products, improve quality and governance, optimize performance, and collaborate with BI, analytics, architecture, product, and data science teams.

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.

Lead the data organization powering frontier AI models. Build and manage a team of data and ML engineers, design web-scale crawling and ingestion architecture, develop pipelines for filtering and deduplication, secure proprietary Australian datasets, and establish data governance, privacy, and security standards. Collaborate with model-training teams to refine data mixtures and quality based on model performance.

Lead the design, development, optimization, and support of enterprise data platforms using Microsoft Fabric. Build scalable batch and near-real-time pipelines, implement medallion architectures, develop SQL/Python/PySpark transformations, and drive governance, security, performance, recovery, and compliance. Provide technical leadership, mentoring, troubleshooting, and stakeholder engagement across cloud transformation, reporting, analytics, and AI initiatives.

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.

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.

Leads the design and delivery of scalable software, APIs, services, platforms, and data-enabled solutions for Business Banking customer and colleague experiences. Influences architecture and engineering standards, applies cloud-native and DevSecOps practices, supports production platforms, mentors engineers, and partners with product, business, and architecture stakeholders. Work may span customer channels, CRM, data services, AI-enabled experiences, and shared platform capabilities.

Leads Azure-based data engineering engagements, including architecture, implementation, assessments, ingestion frameworks, data warehousing, analytics, and proof-of-concepts. Advises clients, presents to executives, supports presales, conducts workshops, reviews architectures, and contributes to delivery planning. Provides technical leadership through mentoring, hiring, onboarding, and engineering best-practice development.

Build and maintain batch and streaming data pipelines, Airflow workflows, data models, and warehouse datasets for a crypto trading platform. Improve data quality, freshness, performance, and cost efficiency through monitoring, validation, troubleshooting, and optimization. Collaborate with engineering and business users to define datasets, metrics, and documentation. This fully remote APAC role requires strong Python and SQL skills, hands-on Kafka, Spark, and Airflow experience, crypto or Web3 data engineering experience, and clear Mandarin communication.

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.

Serves as the primary technical interface for data center thermal solutions across the Pacific region. Leads cooling system architecture, chilled water plant design, chiller selection, BMS integration, technical pricing, bid development, value engineering, and commercial negotiations. Partners with sales, engineering, supply chain, and delivery teams to develop competitive proposals, influence early-stage designs, manage technical governance, and grow cooling infrastructure opportunities.

Leads the design and delivery of reliable data products, production pipelines, transformation frameworks, and data models supporting reporting, analytics, and AI. Diagnoses complex data and performance issues, improves testing, deployment, monitoring, governance, lineage, and reliability, and makes technical decisions across cost, security, and quality. The role also mentors engineers, collaborates with cross-functional teams, reduces technical debt, and improves developer productivity.

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.

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.

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.