Top Data Engineer Jobs in Sydney

The Data Engineer will design and manage data processing systems, transforming raw data into actionable insights by building scalable pipelines on Google Cloud Platform technologies.

The Data Engineer will work on diverse projects, creating ETL processes and unifying client data from various sources while addressing data management challenges and supporting client needs.

Own and maintain Brighte's ELT pipelines, Snowflake data warehouse, and data architecture; implement testing and controls; administer Snowflake; collaborate on real-time pipelines and AI use cases; improve data processes and enable trustworthy, business-ready data.

As a Data Engineer, you will build and maintain data systems for drug safety AI tools, ensuring reliable data processing and storage for research teams.

Intelligen is seeking Data Engineers with 3-5+ years of experience in ETL and DWH technologies, particularly with Snowflake and dbt, to improve client data models and pipelines.

Build and operate the data infrastructure powering reporting, analytics, and AI. Design and maintain dbt models, Dagster pipelines, and ingestion connectors. Scale AWS-based data infrastructure with Terraform, improve orchestration reliability, enable self-service analytics, enforce data quality via CI/CD and testing, and collaborate with analysts and data scientists to support production ML use cases.

Join Speechify's AI team as a Software Engineer responsible for data collection and infrastructure management to enhance model training operations.

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.

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 Lead Data Engineer will design and build cloud engineering solutions, lead data infrastructure projects, and mentor the engineering team to enhance data value for clients.

The role involves designing, building, and deploying Generative AI solutions, contributing to data analytics, and engaging with clients to solve complex business problems.

Lead the design and development of modern data platforms, focusing on scalable architecture and high-performance data pipelines while ensuring security and compliance.

As the ICT - Data Centre Lead, you'll oversee ICT engineering for data centre projects, mentor teams, ensure project delivery, and engage clients.

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 and maintain scalable data architecture, optimize ETL pipelines, ensure data quality and security, and support AI solutions in collaboration with teams.

The role requires overseeing mechanical design for data centres, leading projects, engaging clients, and mentoring junior engineers, while staying updated on industry trends.

The role involves designing and building cloud-native data platforms and pipelines, optimizing data storage, and consulting with clients on data solutions. It requires hands-on engineering skills and experience with modern data technologies.

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.

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.

Build, deploy, and support cloud-native data and analytics solutions on Azure. Design and maintain data pipelines, ingestion, transformation and integration processes using Apache Airflow. Ensure data quality, monitoring, CI/CD and infrastructure-as-code. Troubleshoot platform issues, collaborate with cross-functional teams, and contribute to platform improvements and documentation.

The Data Visualization Developer will create and support dashboards in Power BI, develop SQL scripts, perform regression testing, and troubleshoot existing scripts.

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

In this role, you'll monitor data pipelines, perform quality tests, resolve data incidents, and implement automation for reliability in trading operations.

The Senior Data Engineer will lead data quality assurance, manage and enhance the analytics platform, design self-service data products, deliver analytics capabilities, and mentor junior team members.