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. Senior Data Engineer Hiring Location: San Francisco 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, normalised vs denormalised) 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 We hope you have a basic understanding of AI and enjoy thinking about how data engineering and AI can work together in practical and creative ways. These four areas are the main focuses of the DE team. Ideally, you should be strong in at least one of them — especially data modeling or data ETL. If you also have experience or skills in the other areas, that would be a big plus. 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 qualifications: Bachelor’s degree or higher in Computer Science, Information Systems, Finance, Maths or a related field. Minimum 5 years of proven of experience designing and implementing ETL pipelines using tools such as Informatica, Talend, Apache NiFi, or similar data integration platforms. Proficiency in SQL, database management systems (e.g., MySQL, PostgreSQL, Oracle), and data warehousing solutions. Familiarity with Google Cloud Platform (GCP), specifically BigQuery and Airflow. Excellent problem-solving skills, with a keen attention to detail and a commitment to producing high-quality work. Strong communication and collaboration skills, with the ability to work effectively in a fast-paced, team-oriented environment. Outstanding verbal communication skills, with the ability to effectively collaborate with globally distributed teams. Preferred qualifications: Expe