Data Engineer - Claims Intelligence

About the role

Bupa is looking for a Data Engineer in Melbourne to support Bupa ANZ's Data Strategy and the Claims Intelligence team's Business Intelligence Strategy and Roadmap. The role builds and maintains data platforms that enable strategic use of data across Health Insurance, Health Services and corporate functions, covering operational data and analytics, data warehousing, advanced analytics and self-service.

Key responsibilities

• Migrate data and data assets from legacy systems to strategic platforms while keeping legacy systems stable during the transition.
• Build, transform and maintain scalable data pipelines and datasets aligned to target-state architectures and engineering standards.
• Develop, optimise and support data transformations across operational data stores, data warehouses and Databricks.
• Modernise the data platform, including moving legacy SAS datamarts to a cloud-based Databricks platform, and support dual-platform operations during the transition.
• Monitor and support mature SAS ETL processes, troubleshoot failures and escalate issues when needed.
• Manage data governance, document data models, pipelines, logic and lineage, and proactively manage data-related risks.

Required profile

• Tertiary qualification in Computer Science, Information Systems, Data & Analytics, Engineering or a related field, or equivalent practical experience; postgraduate qualifications are an advantage.
• 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 with Databricks; migrating data pipelines from SAS to Databricks is an advantage.
• Prior experience in private health insurance, especially with claims data, is an advantage.

Required skills

• Data engineering and software development, including building and maintaining scalable data pipelines.
• ETL/ELT design, dimensional modelling (e.g. SCD/SCD2) and data quality, testing and maintainability.
• Business intelligence and analytics tools such as semantic modelling, data cubes and reporting platforms (e.g. Power BI).
• Data governance and the creation of trusted, reusable, decision-ready data assets.