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Airwallex is an AI-native financial operating system serving over 675,000 businesses globally, including McLaren Racing, Qantas, SHEIN, and TikTok. The company operates as a regulated backbone for global payments with 85+ licenses and presence across North America, Europe, the Middle East, and Asia-Pacific. Co-headquartered in San Francisco and Singapore with 2,300+ employees across 27 offices.
The Knowledge Platform team is building the data foundations that support critical business and AI use cases across Airwallex. This role focuses on evolving traditional data infrastructure into knowledge infrastructure—transforming structured and unstructured information into trusted, reusable context for better decision-making by both people and AI systems.
In this position, you will:
- Design and build scalable data pipelines and data models powering critical use cases across Knowledge Platform teams and business domains
- Partner with engineering and cross-functional stakeholders to translate ambiguous data needs into reliable, well-structured solutions supporting decision-making and product development
- Own projects end-to-end, from system and pipeline design through performance monitoring and ongoing improvements in data quality and reliability
- Raise the bar on technical design and execution as a senior team member, bringing strong engineering judgment, practical problem-solving, and high ownership standards
- Work with core technologies including Databricks, Clickhouse, Spark, and Kafka to build real-time analytics, knowledge retrieval and indexing, model infrastructure, agent capabilities, and AI interaction history
- Develop evaluation, annotation, feedback, governance, permission, and freshness frameworks required to operate AI reliably at global financial scale
The role requires five days per week in the Singapore office.
REQUIREMENTS:
- Bachelor's degree or higher in Computer Science, Computer Engineering, or related field
- Minimum 5 years of experience designing and implementing scalable ETL pipelines across batch and real-time workflows using technologies such as DBT and Spark
- Strong proficiency in SQL, database management systems, data warehousing, and data modeling for business intelligence, machine learning, and operational needs
- Strong understanding of data schemas including star, snowflake, normalized, and denormalized designs
- Familiarity with Google Cloud Platform and modern data infrastructure tools including Databricks and Airflow
- Knowledge of data governance practices, the six key governance pillars, and regulatory requirements in the financial industry
- Strong problem-solving, attention to detail, communication, and collaboration skills with experience working with globally distributed teams
- Understanding of AI and interest in applying data engineering and AI in practical and creative ways
PREFERRED:
- Experience with distributed or multi-datacenter systems including data migration, duplication, and consistency challenges
- Experience in financial services, payments, or fintech
- Proficiency with scripting languages such as Python or R for data analysis and automation
- Certification in data management or related technologies
