Senior Data Engineer
Summary
Build and lead scalable data pipelines using Azure Databricks, dbt, and Kimball modeling to power analytics and AI initiatives for an Australian organization.
- Join a quintessentially Australian brand
- Strong experience across Azure Databricks, dbt and Kimball dimensional modelling required
This is an exciting opportunity to take ownership of a growing Data Engineering capability and play a key role in shaping how an organisation leverages data to deliver better decisions, deeper insights and future AI-driven initiatives.
Reporting to the Head of Data & Analytics, the Data Engineering Lead will be responsible for the design, delivery and ongoing evolution of the enterprise data platform. This is a hands‑on technical leadership role, combining strong engineering capability with the opportunity to establish best practices, mentor engineers and influence the future direction of the organisation’s data landscape.
Key Skills & Responsibilities
- Lead Data Engineering activities across data ingestion, transformation, orchestration and data serving capabilities.
- Design, build and optimise scalable data pipelines using Azure Databricks, dbt and Azure Data Factory.
- Own and continuously improve the enterprise data platform using modern Azure data technologies.
- Apply Kimball dimensional modelling principles to deliver trusted and scalable data solutions.
- Develop and support modern data architectures including Lakehouse, Delta Lake and Medallion architecture patterns.
- Drive the adoption of modern incremental data processing frameworks and engineering practices.
- Establish and maintain engineering standards across coding practices, testing, CI/CD, documentation and operational support.
- Lead and mentor a small team of Data Engineering professionals, fostering a culture of collaboration and continuous improvement.
- Build trusted data assets that support business reporting, analytics and AI initiatives.
- Support production data platforms through monitoring, troubleshooting, performance optimisation and continuous improvement.
- Partner closely with BI, Analytics, Data Science and Technology teams to ensure data is structured for business consumption
- Improve data quality, governance and overall engineering maturity.