Airwallex
Senior Data Platform Engineer, Knowledge Platform & AI Enablement
- Jenis pekerjaan
- Penuh waktu
- Cara kerja
- Di kantor
- Lokasi
- SG - Singapore
- Dipublikasikan
- 6 Mar 2026
- Terakhir diperiksa
- 26 Okt 2026
Tentang posisi ini
ABOUT AIRWALLEX
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. 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 2,700 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 https://www.airwallex.com/en-us/operating-principles to see it in full.
ABOUT THE TEAM
The Data Platform Engineering team is going to implement various data processing tasks on the newly built next-generation data platform. We aim to further optimize data storage, processing, application, interfaces, and other dimensions. Throughout the continuous iteration process, we will also incorporate new data components and consider new data architectures (such as Lake House) to achieve faster data scheduling, significantly reducing data latency and enabling more efficient operation of data engineering applications.
HOW YOU'LL MAKE IMPACT
You’ll help build the next generation of Airwallex’s data and knowledge infrastructure, turning structured and unstructured information into trusted context for people and AI systems.
- Own the design, development, and operation of large-scale distributed data systems across batch and streaming workloads, improving data latency, reliability, and engineering productivity.