Data Engineer
Core
Design and build reliable data and machine learning pipelines to enable data-driven decisions for Marketing, Purchasing, Logistics, and Finance teams across Australia's largest eCommerce platform.
Role type
Senior Data Engineer (ML Infrastructure)
Builds
Scalable ETL/ELT pipelines, data models, and MLOps infrastructure for production ML workflows.
Domain
eCommerce, Data Engineering, Machine Learning Infrastructure
Deliverable
production ML models
Required skills
SQL optimization, Python, Pipeline Orchestration (Airflow, dbt, AWS Glue), Cloud Data Platforms (GCP), ML Engineering, Git/CI/CD, Docker
Preferred skills
Event streaming (Kafka, Kinesis), ML platforms (SageMaker, Vertex AI, Databricks), BI tools (Looker, Tableau)
Technologies
BigQuery, Snowflake, GCP, Airflow, dbt, AWS Glue, Python, SQL, Kafka, Kinesis, Docker, Git, CI/CD, SageMaker, Vertex AI, Databricks, Looker, Tableau
Responsibilities
Design and maintain ETL/ELT pipelines handling 10M+ daily events; Develop and optimize data models for analytics and ML training; Build underlying features and data inputs for ML models; Establish and maintain MLOps pipelines for model deployment and monitoring; Work with internal APIs and third-party tools for efficient data ingestion; Implement data quality, security, and documentation best practices; Contribute to AI/LLM experiments for business problem solving.
Seniority
Senior, hands-on IC
