Top Data Engineer Jobs in Sydney

The role is responsible for managing AI infrastructure, building scalable systems for AI features, and collaborating with teams to enhance AI capabilities.

The Expert Software Engineer will lead development of Data Orchestration products, ensuring technical integrity, mentoring teams, and collaborating across departments to drive engineering initiatives and product quality.

As a Software Engineer, you will develop the Feedzai Orchestration solution, focusing on backend engineering, building APIs, and collaborating on user interfaces within an Agile environment.

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

Lead data engineering initiatives by building and maintaining robust data pipelines and customer data platforms, ensuring efficient data management and analysis.

Design and build data models for analytics to connect engagement and performance data. Collaborate with data scientists and optimize queries for insights.

The Senior BI Developer will work on data migration projects, leveraging Alteryx and SQL, focusing on ETL workflows and API integration.

The Senior Data Engineer will develop and maintain data pipelines, define common data standards, integrate data systems, and collaborate with cross-disciplinary teams to enhance data governance and platform scalability.

Lead design and delivery of scalable data architecture and pipelines. Collaborate with stakeholders to integrate multiple data sources, maintain data infrastructure, ensure quality/reliability, and promote data-driven decisions while adopting modern data engineering best practices.

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.

Plan and execute rolling upgrades across tens of thousands of ClickHouse clusters, own the full release pipeline, investigate and resolve production issues (including on-call), and build automation and tooling to make large-scale database operations safer and repeatable while collaborating with core database and cloud infrastructure teams.