Data Engineer
1st July, 2024

 Must be able to obtain Baseline




Flexible work arrangements available


Must be able to obtain Baseline


The Data Engineer will be responsible for delivering professional services, including:

  1. Follow the principles defined­ in the team’s Data Engineering quality blueprint and training materials.
  2. Collaborate with data analysts, data scientists, and business stakeholders to understand data requirements and deliver solutions.
  3. Design, develop, and maintain data pipelines and code-first “extract, load, transform” (ELT) processes using a combination of team standards and best practices.
  4. Optimise pipeline performance through performance-tuning techniques.
  5. Ensure data quality, reliability, and availability across different sources and destinations.
  6. Design and maintain reference data tables according to the team’s reference data standards using manual and automated mechanisms.
  7. Implement defined data governance, security, and privacy policies and standards.
  8. Monitor, troubleshoot, and optimize data pipeline performance and issues.
  9. Write clear, business meaningful, code documentation that explains the intent and mechanics of scripts
Essential criteria

1. Data engineering
a. Ability to create high business value, robust, and efficient SQL data pipelines to curate data from complex and high-volume sources into semantic models for self-service visualisation
b. Ability to create complex data models using SQL and cloud
c. Ability to create automatic testing of data quality and model outputs
d. Experience in a GIT process development environment

2. Customer focus Ability to communicate effectively to technical and non-technical audience

3. Team player Ability to work openly and collegiately in a multi-disciplinary team displaying the characteristics of creativity and resilience

Desirable criteria

1. Design incremental extractions in SQL and partitioned refreshes in Power BI.

2. Familiarity with Australia’s biosecurity and export systems or corporate data systems will be highly regarded.

3. Performance tuning – ability to maintain the optimal execution performance of a complex data pipeline including daily monitoring of the pipeline execution, and application of performance tuning

You can’t apply as it’s expired.