Azure Data Platform Lead Engineer

Colonial Group; Sydney; Híbrido; AUD 170.000 - 210.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Sydney hybrid work

Learning & development

Parental leave 20 weeks

Fitness & savings

Wellbeing support

Descripción de la vacante

Levande is seeking an experienced Data Platform Engineer to help extend and optimise the Azure-based Levande Data Platform (LDP). You will combine hands-on engineering with technical leadership, building ingestion, storage and transformation layers to support reporting, analytics and AI.

Reporting to the Head of Data Platform, you’ll develop production pipelines, data warehouses and data lakes, while improving reliability, performance and data quality.

Formación

• Extensive data engineering experience with SQL and data integration.
• Experience building production pipelines, data warehouses and data lakes.
• Proficiency with Azure data services and Databricks/Spark.
• Ability to design secure, scalable data systems and monitor performance.
• Experience with Scrum or Kanban and Jira/Confluence.

Responsabilidades

• Extend and optimise the Azure-based LDP infrastructure and governance.
• Build and maintain production pipelines, data warehouses and data lakes.
• Improve availability, security and data quality while controlling costs.
• Collaborate with analytics engineers and business teams on data products.
• Guide vendor delivery and act as escalation point for DataOps.

Conocimientos

SQL

Data integration

Production pipelines

Azure

Databricks

Spark

Azure Data Factory

CI/CD

Jira

Confluence

Herramientas

Databricks

Azure

Azure Data Factory

Git

CI/CD pipelines

Descripción del empleo

Levande is seeking an experienced Data Platform Engineer to help extend and optimise the Azure-based Levande Data Platform (LDP). You will combine hands-on engineering with technical leadership, building ingestion, storage and transformation layers to support reporting, analytics and AI.

Reporting to the Head of Data Platform, you’ll develop production pipelines, data warehouses and data lakes, while improving reliability, performance and data quality.