Senior Manager, Data Platform Engineering
Airwallex · US - San Francisco
About this role
**Senior Manager, Data Platform Engineering** **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 Airwallex (directly or via partners) to run financial operations or build and monetize financial products. Founded in Melbourne in 2015, Airwallex is the regulated backbone behind global payments, with 85+ licenses and a financial infrastructure spanning North America, Europe, the Middle East, and Asia-Pacific. Co-headquartered in San Francisco and Singapore, the company has 2,300+ people across 27 offices. **About the Team** This team designs intelligent, autonomous systems that eliminate financial operations for companies globally—automating bookkeeping and reconciliation, tax, payroll, forecasting, and document understanding. They operate with high standards, are tool-agnostic, and expect engineers to own problems end-to-end. **About the Role (San Francisco)** As a **Senior Manager of Data Platform Engineering**, you’ll lead the team responsible for the data backbone of Airwallex’s financial systems. You’ll set the technical direction for high-volume financial data ingestion, the Databricks lakehouse, data orchestration, canonical financial models, and the infrastructure that makes the data platform reliable, scalable, and increasingly real-time. You’ll combine technical judgment with people leadership—building a high-performing engineering organization, establishing platform standards, driving major migrations, and partnering closely with product, accounting, and platform teams. **What You’ll Do** - Lead and grow a high-performing data platform engineering team (technical direction, mentorship, career development, clear ownership) - Set technical strategy for high-volume financial data ingestion (payment processors, banks, billing systems, ERP/GL) with guarantees around correctness, replayability, and recovery - Lead evolution of the Databricks lakehouse using Spark, Delta Lake, Unity Catalog, dbt, Python, and SQL—optimizing for scale, performance, reliability, and cost - Define standards for data contracts, schema evolution, drift detection, data quality, and canonical financial models - Evolve the platform from batch toward near-real-time using Kafka, CDC, micro-batching, incremental processing, and Dagster orchestration - Own the architecture of canonical financial models that transform source data into consistent financial objects and relationships powering the accounting engine - Partner with infrastructure and platform teams on production foundations: observability, alerting, incident response, recovery, and operational readiness - Guide infrastructure strategy across GCP, Terraform, Kubernetes/GKE, Helm, GitOps, and CI/CD - Lead migrations from legacy pipelines to the lakehouse with parity validation, staged rollouts, and clear rollback strategies - Drive performance and cost optimization across PostgreSQL, Databricks, and large-scale data workloads - Partner with product, accounting, ML, and platform leadership to translate business needs into scalable data platform capabilities - Set technical direction through architecture reviews, design documents, engineering standards, and long-term platform roadmaps - Champion AI-assisted engineering practices while maintaining high standards for code quality, testing, security, and reliability **What You’ll Need to Have** - **8+ years** e
Listing freshness
CronJobs last confirmed this listing 1h ago. If its source stops confirming the opening for seven days, this page is removed from active inventory.