Data Engineer – Build ELT pipelines and AI‑ready data sets

About the role

We are partnering with a major Australian multi‑site retail and franchise network to build their data and AI capabilities from the ground up. As a hands‑on Data Engineer in the newly formed Data & Insights team, you will create and maintain the pipelines, models and reporting foundations that power commercial decision‑making across a massive national footprint.

Key responsibilities

• Build and maintain ELT pipelines that move data from legacy ERPs, e‑commerce and marketing platforms into Snowflake.
• Develop dbt models using a medallion architecture and design dimensional models (customer, product, location, inventory) to support reporting and AI use‑cases.
• Work with Azure Data Factory and ADLS for ingestion pipelines and configure iPaaS integrations to automate data movement.
• Troubleshoot data‑quality issues, implement validation and cleansing logic, and support Power BI reporting.
• Apply CI/CD, version‑control and testing practices to the data platform while collaborating with data leadership, BAs and business stakeholders.

Required profile

• 5–8 years of data‑engineering experience with a focus on pipeline delivery, data quality and master‑data management.
• Strong SQL and Python skills; comfortable using Python via Snowpark for complex logic.
• Proven experience with Snowflake, dbt (including testing frameworks) and Azure Data Factory/ADLS.
• Hands‑on experience configuring iPaaS integrations and building reference data sets.
• Deep understanding of dimensional modelling (Kimball methodology) and ELT design patterns.

Required skills

• SQL
• Python (including Snowpark)
• Snowflake
• dbt
• Azure Data Factory
• Azure Data Lake Storage (ADLS)
• iPaaS integration tools
• Power BI
• Git
• CI/CD pipelines