Top Data Engineer Jobs in Melbourne

Design, build, and operate scalable data platform pipelines and models using Snowflake, dbt, Prefect, Flink and AWS. Own end-to-end platform components, apply IaC with Terraform, implement CI/CD and testing, manage production systems, monitoring and incident response, and improve data quality and self-service capabilities for downstream teams.

Design and build scalable data platform solutions from ingestion to modelling and infrastructure. Own end-to-end technical problems, improve data quality and observability, implement IaC and CI/CD, manage production systems (monitoring, incident response), and enable self-service analytics for downstream teams using modern tools like Snowflake, dbt, Prefect, Terraform, AWS and real-time tech.

Lead architecture and technical direction for real-time streaming and analytics. Own data ingestion, streaming, modelling, warehousing and data apps. Influence cross-team standards, mentor engineers, review designs, and improve operational reliability and DataOps to deliver scalable, secure data products.

The Principal Data Engineer will design and deliver scalable data architectures, optimize data pipelines, and guide clients in data management strategies. Responsibilities include technical leadership, collaborating with teams, and translating complex concepts for diverse audiences.

The Principal Data Engineer will architect cloud-native platforms, oversee model execution and data management while ensuring compliance with regulatory standards in a high-performing team.

Lead design and delivery of scalable data and AI platforms across Azure and Databricks. Architect production-grade ML/LLM solutions (RAG, agentic patterns), build batch and streaming pipelines, formalize medallion/lakehouse standards, implement MLOps/DataOps (CI/CD, monitoring, orchestration), embed Responsible AI and governance, and provide technical leadership, mentoring, and cross-functional delivery.

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, develop, and maintain ELT/ETL Snowflake data warehouse solutions; implement data standards, CI/CD, monitoring, and production support; collaborate with product, business, and vendors; mentor junior developers and improve data engineering practices.

Design and deliver scalable multi-cloud data solutions using Databricks/Snowflake and modern data stacks. Build ingestion, transformation, modelling and access layers, implement dbt and CI/CD, ensure data quality, governance and lineage, support analytics and AI enablement, and consult directly with clients while mentoring junior engineers.

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

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

As a Lead Data Engineer, you'll design and maintain platform capabilities, CI/CD pipelines, and cloud infrastructure, ensuring system reliability and developer productivity while utilizing AI tools effectively.

As a Senior Data Engineer, you will design and implement data transformation pipelines, gather business requirements, and work with various big data technologies.

The Senior Data Engineer/Consultant will lead data engineering projects, ensuring high-quality technical outcomes using Databricks, while mentoring team members and collaborating with clients.

Lead design and delivery of modern cloud data platforms and scalable pipelines. Improve platform stability, implement data models and governance, support data product development, mentor junior consultants, and collaborate with stakeholders to translate business needs into reliable, automated data solutions.

As a Data Engineer, you will develop and manage data pipelines, ensuring reliable data ingestion and delivery while optimizing for simplicity and performance.

Build and own Overflow's data infrastructure: design ETL/ELT and CDC pipelines, data warehouses, OLAP schemas, and a unified data layer. Enable self-service analytics and dashboards, implement Databricks Unity Catalog for governance, ensure data quality and monitoring, and partner with Head of AI and cross-functional teams to prepare semi-structured data for MLOps and advanced AI workflows.

This role involves sourcing audio data, managing cloud infrastructure, collaborating on data processing workflows, and contributing to dataset roadmaps.

The Sr Data Engineer will optimize and refactor data pipelines, manage data ingestion from APIs, and support product engineering teams. Responsibilities include design best practices, enhance the modern data stack, and troubleshoot data issues, ensuring resilience and operational excellence.

Build and improve backend and data pipelines to fetch and analyze smart meter time-series data, develop classical ML models to size batteries, prioritize features that drive revenue, and collaborate with founder, UX, and contractors to ship experiments rapidly.

Build and improve a platform that ingests smart-meter time series data, models household energy consumption, simulates battery performance, and prioritises features that drive revenue. Work across backend (Python/Clojure), frontend (TypeScript), time-series databases, and classical ML models to deliver production-ready analyses and experiments; collaborate with founder, UX specialist, and overseas developers; possibly manage a contractor.

Design and maintain scalable data pipelines and real-time streaming systems on AWS, enable ML/LLM-powered customer features, integrate Amazon Bedrock and models into agentic workflows, automate ML lifecycle steps, collaborate with ML, data science, and product teams to ship production-grade solutions.

Build and enhance enterprise analytics and visualization solutions: develop interactive dashboards, data-driven apps, transform and prepare data with Python, implement frontend visualization components with TypeScript, integrate with Microsoft and Google enterprise ecosystems, and use AI tools to accelerate development while collaborating with stakeholders and owning deliverables end-to-end.

The Senior Data Engineer will engineer and improve enterprise-scale data platforms on AWS, design and build end-to-end data pipelines, and optimize data processing solutions using Apache Spark and Python. Responsibilities include data governance, compliance controls, and collaboration with cross-functional teams to support analytics and data integration needs.