Staff Data Engineer, Data and AI, Customer Data Platform

Location: Singapore; in-office five days a week

Company

Airwallex is an AI-native financial operating system and regulated infrastructure provider for global payments and financial products.

What you will do

• 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 for operational needs, agentic workflows, machine learning, and business intelligence.
• Design and apply star, snowflake, normalised, and denormalised schemas.
• Apply data engineering and AI best practices to solve practical problems in scalable ways.
• Partner with globally distributed teams on complex technical work.

Requirements

• 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 star, snowflake, normalised, and denormalised data schemas.
• Excellent analytical and problem-solving abilities, attention to detail, communication, and collaboration skills.
• Understanding of AI and interest in applying data engineering and AI in practical and creative ways.

Nice to have

• Familiarity with Google Cloud Platform and modern data infrastructure tools, including Databricks and Airflow.
• Experience working with globally distributed teams.

Culture & Benefits

• In-office work from the Singapore office five days a week.
• Collaboration with globally distributed teams across Airwallex’s international organisation.
• Equal-opportunity employment based on merit, qualifications, competence, and talent.