Senior Data Engineer, Data and AI, Customer Data Platform
Summary
Senior Data Engineer at Airwallex, a fintech, building the data foundations for its Customer Data Platform: developing data models and owning scalable batch/streaming ETL pipelines that power analytics, machine learning and AI. Core stack includes SQL, Databricks, dbt, Spark, Airflow and GCP.
Senior Data Engineer, Data and AI, Customer Data Platform
Airwallex is the AI-native financial operating system for a real-time, intelligent economy. More than 675,000 businesses, including McLaren Racing, Qantas, SHEIN, and TikTok, use us, directly or through our platform partners, to run their financial operations or build and monetize financial products of their own. We started in Melbourne in 2015 to build the infrastructure global commerce runs on.
Skills, with evidence
Develop data models and data products that support operational needs, agentic workflows, machine learning, and business intelligence including star, snowflake, normalised, and denormalised designs. must have
- SQL Strong proficiency in SQL, database management systems, data warehousing, and data modelling for business intelligence, machine learning, and operational needs. must have
- Spark Own scalable ETL pipelines across batch and real-time workflows, using technologies such as Databricks, DBT, and Spark. must have
- Streaming At least five years of experience designing and implementing scalable ETL pipelines, including batch and streaming workflows. must have
- Airflow / orchestration Familiarity with Google Cloud Platform and modern data infrastructure tools, including Databricks and Airflow.
- Machine learning 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. not practised here
- Warehousing - Strong proficiency in SQL, database management systems, data warehousing, and data modelling for business intelligence, machine learning, and operational needs
- dbt - Own scalable ETL pipelines across batch and real-time workflows, using technologies such as Databricks, DBT, and Spark
- GCP - Familiarity with Google Cloud Platform and modern data infrastructure tools, including Databricks and Airflow not practised here