Top Data Engineer Jobs in Melbourne

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

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.

Build and support high-volume real-time data pipelines using Amazon Kinesis, Databricks, Spark Structured Streaming, and Delta tables. Optimize pipelines for performance, latency, reliability, and cost; implement CloudWatch monitoring and alerting; integrate streaming and batch systems; write testable Python and SQL; and troubleshoot data-processing issues. Candidates must be Australian citizens with an active security clearance.

Build and administer secure, scalable Databricks environments on AWS. Responsibilities include configuring Unity Catalog and governance controls, designing VPC and PrivateLink network architectures, managing IAM and access models, developing production data pipelines, implementing Terraform infrastructure as code, establishing CI/CD deployments, and enforcing cluster policies and platform standards.

Own EULER’s data lifecycle, including instrumentation, validation, reconciliation, privacy, anonymization, canonical data modeling, analytics datasets, documentation, and production pipelines. The role requires strong SQL, software engineering practices, independent ownership, skepticism toward data quality, and clear communication. Preferred experience includes GDPR/LGPD, multi-tenant B2B SaaS, dbt, statistics, Cloudflare Workers, Postgres, Parquet, DuckDB, and Bubble.

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.

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.

Develops and maintains enterprise data platforms, pipelines, integrations, data models, and analytical datasets. Builds ETL/ELT processes in Microsoft Fabric, supports Power BI reporting, improves data quality and governance, monitors platform reliability, troubleshoots incidents, and enables self-service analytics. Collaborates with technical and business stakeholders to translate requirements into scalable data solutions, automate processes, document data assets, and continuously improve platform performance and trust.

Design and deliver scalable data pipelines, models, and reusable solutions using Microsoft Fabric and Azure technologies. Apply AI, automation, DevOps, security, privacy, governance, and data-quality practices to large enterprise datasets. Partner with stakeholders to define requirements, manage delivery risks, and produce reliable data solutions. Provide technical leadership, coach engineers, and advance data engineering standards and practices.

Leads the design, development, optimization, and support of enterprise data platforms using Microsoft Fabric. Builds scalable batch and real-time pipelines, medallion architectures, data transformations, governed cloud solutions, and reporting infrastructure. Oversees performance tuning, security, disaster recovery, monitoring, and production support while providing technical leadership, mentoring, and stakeholder engagement across data modernization and transformation initiatives.

Operate, monitor, maintain, and improve MPLS, data center, and global WAN networks. Configure and deploy carrier-grade infrastructure, troubleshoot outages and escalated incidents, support automation using Python and CI/CD, and contribute to network design, documentation, risk management, and project delivery. The role includes Linux administration, stakeholder collaboration, and participation in an after-hours on-call roster.

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.

Leads technical pre-sales for enterprise data management solutions, including discovery, solution design, product demonstrations, proof-of-concept engagements, executive briefings, RFP responses, and partner enablement. Translates customer requirements involving cloud infrastructure, data security, governance, privacy, and compliance into measurable business outcomes. Collaborates with sales, product, engineering, marketing, customer success, and channel partners throughout complex enterprise sales cycles.

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.

Operate and support data platforms by monitoring pipelines and ETL processes, investigating data and reporting issues, responding to alerts, troubleshooting incidents, and improving monitoring, runbooks, and automation. The role uses SQL and supports cloud, Kubernetes, legacy platforms, infrastructure-as-code, CI/CD, and Confluent event streaming. After training, the engineer will participate in a 24/7 on-call roster.

Provide hands-on technical leadership for scalable Payments data platforms, products, and pipelines. Design batch and streaming solutions using AWS, Snowflake, Spark, Airflow, and Glue; influence architecture and engineering standards; establish data quality, resilience, observability, and reliability practices; mentor engineers; and partner with architecture, product, security, and data consumers to deliver reusable cloud-native data capabilities.

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 REA Group’s Data Mesh implementation across multiple squads and domains. Define technical strategy, architecture, governance, and platform capabilities that enable discoverable, trustworthy, secure, and useful data products. Partner with engineering and business leaders, validate solutions, solve complex operational challenges, explore emerging data technologies, and mentor experienced engineers. The role requires hands-on technical leadership, cross-functional influence, cloud expertise, and long-term architecture planning.

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.

Develop and optimize Supabase’s Data API based on PostgREST. Responsibilities include improving the PostgreSQL driver and performance, developing features, fixing bugs, resolving incidents and escalated support issues, improving observability, and debugging integrations across the Supabase platform. The role requires strong Haskell development, PostgreSQL and SQL expertise, Linux debugging, open-source contribution, and technical user support.

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.

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.