Top Data Engineer Jobs in Sydney

Migrate SQL Server data products and analytical workloads to a modern cloud platform. Redesign legacy SQL, ETL processes, and business logic; build scalable pipelines and analytical models; validate data quality; collaborate with stakeholders; and improve platform reliability, performance, cost efficiency, and AI-assisted data engineering automation.

Design, deploy, and maintain AWS and Databricks data infrastructure, data lakes, and scalable ETL pipelines. Build custom ingestion services and connectors using Airbyte, Go, and Python; optimize performance, reliability, monitoring, and data quality. Implement cloud data governance, security, access controls, encryption, and compliance practices while collaborating with platform engineers, analysts, and stakeholders.

Lead design and scaling of real-time data infrastructure. Build fault-tolerant ETL/ELT pipelines, implement Kafka/RabbitMQ streaming, optimize ClickHouse schemas and queries, deploy secure cloud data services, mentor engineers, and implement monitoring and automated recovery for financial analytics.

Design and maintain StratFin data models, automate reporting and data workflows, build reliable pipelines with testing and monitoring, partner with Finance and Strategy to deliver scalable data products, and establish data quality standards to enable analytics and AI.

Develop, deploy, and maintain LLM pipelines and RAG QA/search systems; design and optimize prompts and multi-agent LLM architectures; operate multi‑GPU/cluster inference; build evaluation pipelines for model quality, bias, and hallucination; collaborate with product and CS teams to integrate conversational AI.

Triage and resolve Master Data Management (Party, Risk, Reference) issues in Stibo STEP, run targeted data-cleansing sprints, monitor data quality dashboards, support releases/UAT, and maintain documentation and playbooks to prevent recurring data issues.

Lead design, development, deployment, and continuous improvement of cloud-native data and analytics solutions on Azure. Act as a hands-on technical lead building scalable, secure end-to-end data platforms using Spark/Databricks, Python/PySpark, and orchestration tools. Ensure CI/CD, DevOps, automation, code quality, mentor team members, resolve complex technical issues, and collaborate with stakeholders and vendors to drive platform evolution.

Lead design and delivery of cloud data platform solutions for banking clients. Own solution design, data modelling, ETL/CI-CD, SQL development, stakeholder engagement, and lead technical project delivery.

As a Python and Kubernetes Software Engineer, you will develop data analytics solutions using open source tools, collaborating with a distributed team.

Design, build and maintain scalable cloud data pipelines and warehouses. Improve performance, ensure data quality and governance, support AI/ML initiatives, document systems, and collaborate with cross-functional teams while mentoring engineers.

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.

Serve as the primary technical interface for data centre cooling, leading chilled water and liquid cooling solution design, technical pricing, and bid management. Influence early-stage design, ensure performance and resilience, develop cost models and value-engineered proposals, and align cross-functional stakeholders to win complex thermal infrastructure projects.

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 operate scalable Lakehouse data platform and batch/real-time pipelines. Develop data assets, streaming solutions (Kafka/Flink), ETL/ELT workflows (Airflow, DBT), and cloud-native architectures on AWS, Snowflake and Databricks. Optimize SQL and Spark workloads, manage schema and performance for Iceberg tables, enforce data quality and governance, and partner with data science, analytics and product teams to deliver cost-efficient, reliable data solutions.

Design, build, and operate scalable batch and real-time data pipelines and Lakehouse data assets using AWS, Snowflake, Databricks, Kafka, and Flink. Collaborate with data science, analytics, and product teams to ensure data quality, modelling, governance, performance, and cost-efficiency.

Designs, builds and supports Microsoft Power Platform applications and data integrations. Develops data models, performs system migrations, documents solutions, provides user support and training, and applies engineering practices for version control, testing and deployments to improve project and operational delivery.

Design and lead a cloud-agnostic enterprise data ingestion framework: author solution designs, build and optimize batch/streaming pipelines, embed data quality/lineage/security, and guide engineering squads for scalable multi-cloud data platforms.

Design and implement scalable cloud-native data architectures, build and optimise ETL and big-data pipelines using Databricks/Spark/Delta Lake, ensure data quality and governance, automate workflows with Python/Scala/SQL, and collaborate with data scientists and analysts to enable AI-driven insights.

The role involves designing and building scalable systems for trade lifecycle management, maintaining data integrity, and collaborating on reporting pipelines, while also handling production support.

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.