The role
- Designs, develops and maintains assigned data pipelines and processes across the Azure Synapse-based data platform.
- Maintains assigned DevOps pipelines that automate the testing, deployment and release of data solutions across environments.
- Builds trusted, reusable data products using sound data engineering, testing and documentation practices.
- Escalate significant design, security, governance and delivery risks to the Lead Data Engineer.
- Works collaboratively with analytics engineers, analysts, business stakeholders and technology teams to translate data requirements into practical technical solutions.
- Contributes to established engineering standards, reusable patterns, technical documentation and continuous improvement across the data platform.
- Applies and promotes data governance practices in the development of data products, including data quality, documentation, lineage and appropriate use of data.
Job description
The APS6 Data Engineer designs, builds and maintains trusted data products that make enterprise data easier to find, understand and use. Working as part of a multidisciplinary data team, the role transforms data from a range of source systems into well-structured, reliable and reusable datasets that support analytics, reporting and emerging AI use cases.
Eligibility requirements
- Demonstrates technical capability in data engineering, such as Python, Spark, SQL, data modelling, transformation and the development of production data pipelines.
- Applies contemporary engineering practices such as version control, automated testing, CI/CD, documentation and code review.
- Experience with big data systems such as Azure Synapse, Databricks, Fabric or Snowflake.
- Develops high-quality solutions that consider data quality, security, governance, maintainability and performance throughout the data lifecycle.
- Analyses complex data and technical problems, exercises sound judgement and delivers practical solutions with limited direction.
- Communicates effectively with technical and non-technical stakeholders and contributes positively to a collaborative, multidisciplinary team.
- Demonstrates curiosity and initiative in using AI and emerging technologies to improve productivity, streamline data engineering processes and deliver more efficient solutions.