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.

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.

Design, build and optimise cloud data platforms and lakehouse solutions on Microsoft Azure and Microsoft Fabric. Develop scalable ETL/ELT pipelines, data models, and transformations using SQL, Spark and Python. Implement data governance, security, CI/CD and collaborate with stakeholders to enable analytics, reporting and AI use cases.

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.

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.

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, 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.

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

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.

Own and maintain Blinq's data analytics infrastructure and pipelines. Integrate data from multiple sources, build ETL processes, enforce data quality and validation, design scalable data processing systems, maintain dashboards, and partner with Product and Engineering to enable data-driven decisions and reporting.

Design, build and evolve cloud-native, scalable data platforms, products and pipelines to power customer engagement, analytics and AI. Lead data architecture, engineering standards, automation, observability and mentor engineers while partnering with product, risk and business stakeholders to deliver outcomes.

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

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

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.

Design and deliver scalable batch, streaming and analytical data pipelines and reusable semantic data products. Automate data product lifecycles, implement data matching, cleansing, quality and observability controls, support ML productionisation, build CI/CD with GitHub Actions, and provide technical leadership and governance for secure, resilient data solutions.

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.

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.