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, build and maintain batch and near-real-time data pipelines and data products. Develop ingestion, transformation and modelling solutions using SQL, Python, dbt and Spark. Implement data-quality, monitoring, CI/CD and security controls. Support production operations, incident remediation, documentation, and collaboration with product, analytics and platform teams to deliver reliable, performant, and cost-efficient data solutions.

Design, build, and maintain data pipelines and core customer datasets to consolidate legacy systems into a unified customer profile. Collaborate with data engineers, analysts, and cross-functional squads to ensure data accuracy, remediation, and enable personalization and ML capabilities. Work in a small, dynamic team operating within a hybrid Melbourne-based environment.

The Data Engineer will design, build, and manage scalable data pipelines on GCP, ensuring data quality and operational excellence while collaborating with data teams.

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.

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.

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.

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.

Lead design and delivery of cloud-native data platforms and pipelines for regulatory reporting. Build reusable data assets, integrate diverse sources, enforce coding standards, mentor engineers, and ensure scalable, high-performance, reliable data solutions on AWS/Snowflake.

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.

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.

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.

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.

Lead design, build and optimise enterprise data platform and pipelines (Microsoft Fabric migration, AX ERP modelling, Denodo). Ensure platform performance, governance, incident management, and mentor junior/offshore engineers while driving stakeholder delivery in an agile environment.

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 data pipelines, models and APIs across ingestion, transformation and consumption. Lead technical design and architecture, resolve data quality and performance issues, support production with monitoring and automation, and mentor engineers. Work with Airflow/Breeze, BigQuery, Kafka, TypeScript/Node or Python, and cloud/DevOps tooling to deliver reliable, scalable property data platforms and APIs.

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, build and optimise cloud-native, enterprise-scale data platforms and pipelines using Snowflake and AWS. Deliver trusted analytics-ready datasets, improve performance/resilience, drive automation and engineering best practices, support production systems, and mentor colleagues to enable analytics, AI, fraud detection and customer intelligence.

Design, build and operate Snowflake‑centric cloud data platforms and scalable ETL/ELT pipelines. Drive DataOps practices, data quality, governance, security, and observability. Influence architecture, mentor peers, and deliver fit‑for‑purpose analytics solutions in a regulated enterprise environment.

Design and build scalable cloud-native data pipelines on AWS and Snowflake to support financial crime monitoring, reporting and insights. Collaborate with engineers and stakeholders to deliver secure, resilient data products, implement CI/CD automation, and ensure data governance and quality.

Lead design and delivery of scalable AWS-native data platforms and pipelines. Build data products using Glue, Iceberg, Redshift, Airflow and related tooling. Provide technical leadership, governance, automation, and mentoring to a team of data engineers while ensuring security, cost-effectiveness, and cross-team collaboration.