Senior Data Engineer

About Airwallex Airwallex is the AI-native financial operating system for a real-time, intelligent economy. More than 676,000 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. We're the regulated backbone behind global payments: not by accident, but by design. A decade plus, 85+ licenses, and a financial infrastructure spanning North America, Europe, the Middle East, and Asia-Pacific. We're co-headquartered in San Francisco and Singapore, with more than 2,300 people across 27 offices. We hire builders with founder-level energy, people who move fast with good judgment, dig in with real curiosity, and make calls from first principles rather than waiting to be told what to do. Read our operating principles to see it in full. About the team The Knowledge Platform team is at the heart of our company's data and AI strategy. We are building the foundational infrastructure that empowers the entire company to leverage data, AI, and ML into business impact. We accomplish this by creating platforms that handle the entire data and AI/ML lifecycle, simplifying the interface while providing proper safety and governance. What you’ll do You’ll design and build scalable data pipelines and data models that power critical use cases across Knowledge Platform teams and business domains. You’ll partner closely with engineering and cross-functional stakeholders to turn ambiguous data needs into reliable, well-structured solutions that support decision-making and product development. You’ll own projects end to end, from system and pipeline design to performance, monitoring, and ongoing improvements in data quality and reliability. As a senior member of the team, you’ll help raise the bar on technical design and execution by bringing strong engineering judgment, practical problem-solving, and a high standard of ownership. This role is based in Singapore . Responsibilities: Part 1. Data Modeling Design and implement robust and scalable data models that support business intelligence, machine learning, and operational needs. Possess a deep understanding of data schemas and be able to select appropriate schema designs (e.g., star schema, snowflake, normalized vs denormalized) based on use cases. Collaborate closely with business teams to translate their data needs into clean, structured, and well-documented models. Understand and promote the concept of SSOT (Single Source of Truth) throughout the data layers and pipelines. Maintain data consistency, traceability, and quality across multiple data sources and domains. Part 2. ETL and Data Pipeline Management Experience building and maintaining both batch and streaming ETL pipelines, with a strong understanding of end-to-end data workflow — from data ingestion to transformation and delivery. Able to work closely with Data Platform Engineers (DPEs) and Product Managers (PMs) to quickly identify root causes of data issues and provide efficient, scalable solutions. Bonus if you’ve worked with data across distributed or multi-datacenter systems, including solving challenges related to data migration, duplication, and consistency. Part 3. Data Governance Participate in and contribute to data governance strategies, policies, and standards. Be familiar with any of the six key pillars of traditional data governance (e.g., data quality, data stewardship, metadata management, master data management, data privacy/security, data lifecycle). Part 4. Data + AI Understanding of AI and enjoy thinking about how data engineering and AI can work together in practical and creative ways. This role is based in Singapore. Who you are We're looking for people who meet the minimum qualifications for this role. The preferred qualifications are great to have, but are not mandatory. Minimum qualifica