Description
You'll build the data foundations that power Airwallex's Customer Data Platform, helping teams turn customer information into trusted, reusable data for analytics, machine learning, operational decision-making, and practical AI applications.
Responsibilities:
- Own scalable ETL pipelines across batch and real-time workflows, using technologies such as Databricks, DBT, and Spark
- Build reliable data foundations across data warehouses, data consumption platforms, and modern cloud data infrastructure
- Develop data models and data products that support operational needs, agentic workflows, machine learning, and business intelligence including star, snowflake, normalised, and denormalised designs
- Apply data engineering and AI best practices to solve practical problems in creative and scalable ways
- Partner with globally distributed teams to deliver complex technical work with strong problem-solving, attention to detail, communication, and collaboration
Requirements:
- A bachelor’s degree or higher in Computer Science, Information Systems, Mathematics, or a related field
- At least seven years of experience designing and implementing scalable ETL pipelines, including batch and streaming workflows
- Experience with streaming technologies such as Apache Kafka or Apache Flink
- Strong proficiency in SQL, database management systems, data warehousing, and data modelling for business intelligence, machine learning, and operational needs
- Solid understanding of data schemas, including star, snowflake, normalised, and denormalised designs
- Excellent analytical and problem-solving abilities, attention to detail, and effective communication and collaboration skills
- An understanding of AI and an interest in applying data engineering and AI in practical and creative ways
Preferred qualifications:
- Familiarity with Google Cloud Platform and modern data infrastructure tools, including Databricks and Airflow
- Experience working with globally distributed teams