As a Data Engineer at Bupa, you will apply your specialist skills to support Bupa ANZ in executing its Data Strategy, as well as the Claims Intelligence team in delivering the Business Intelligence Strategy and Roadmap. Together, we are focused on building and maintaining Bupa’s data platforms, enabling the organisation to work more strategically with data and decision-ready assets to maximise competitive performance. Data Engineers play a critical role in enabling our data-driven future by contributing to a range of Data Services, including Operational Data and Analytics, Data Warehousing and Business Intelligence, Advanced Analytics, and Self-Service capabilities. This role is important as it underpins high-quality decision-making, unlocks value from data assets, and supports innovation across the organisation. These services span multiple business units, including Health Insurance, Health Services, and corporate functions, making the role central to delivering impactful outcomes. You will support the migration of data and data assets from legacy systems to strategic platforms, while ensuring stability and ongoing support to the legacy systems throughout the transition period. Additionally, you will deliver data solutions aligned with our Target State Architectures across a variety of technologies. You will be responsible for implementing solutions that adhere to established standards and engineering guidelines, while also contributing to the continuous improvement of team practices. What you’ll need to make it possible:

• Tertiary qualification in Computer Science, Information Systems, Data & Analytics, Engineering, or a related field, or equivalent practical experience. Postgraduate qualifications are advantageous but not essential. 
• At least 2 years of hands-on experience developing, managing and supporting SAS ETL processes, including operational monitoring and troubleshooting. 
• At least 1 year of hands-on data engineering experience using Databricks, with experience migrating data pipelines and workloads from SAS to Databricks considered an advantage. 
• Prior experience in the private health insurance sector, particularly with claims data, is advantageous. 
• Strong expertise in data engineering and software development, including building, transforming, and maintaining scalable data pipelines and datasets. 
• Exposure to business intelligence and analytics tools, including semantic modelling, data cubes, and reporting platforms (e.g. Power BI or equivalent). 
• Experience developing and maintaining analytical data assets such as curated datasets, data marts, and semantic layers. 
• Understanding of data modelling concepts (e.g. SCD/SCD2 dimensional modelling) and best practices in data quality, testing, and maintainability. 
• Understanding of data governance principles, including the creation and management of trusted, reusable, decision-ready data assets.