Senior Manager, Data Platform - T0

US - San Francisco · 8+ years experience · Full-time · Hybrid · Posted Oct 1, 2026

At a glance

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 https://www.airwallex.com/en-us/operating-principles to see it in full.

As a Senior Manager of Data Platform Engineering, you'll lead the team responsible for the data backbone of our financial systems.
You'll set the technical direction for high-volume financial data ingestion, our Databricks lakehouse, data orchestration, canonical financial models, and infrastructure.
You'll combine strong technical judgment with people leadership to build a high-performing engineering organization and establish platform standards.

Lead and grow a high-performing data platform engineering team, providing technical direction, mentorship, career development, and clear ownership.
Set the technical strategy for high-volume financial data ingestion across payment processors, banks, billing systems, and ERP/GL platforms, with strong guarantees around correctness, replayability, and recovery.
Lead the evolution of our 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 processing using Kafka, CDC, micro-batching, incremental processing, and Dagster orchestration.
Own the architecture of canonical financial models that transform complex source data into consistent financial objects and relationships powering our accounting engine.
Partner with infrastructure and platform teams to build reliable production foundations across 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 rigorous parity validation, staged rollouts, and clear rollback strategies.
Drive performance and cost optimization across PostgreSQL, Databricks, and large-scale data workloads.

8+ years of experience in data platform, backend, or infrastructure engineering, including significant experience leading engineering teams.
Proven experience managing and developing senior engineers and technical leads and building strong, autonomous teams.
Deep expertise in data platforms, distributed systems, data modeling, and production data pipelines.
Strong experience with Python and SQL, plus hands-on experience with Spark, Databricks, Delta Lake, dbt, and modern lakehouse architectures.
Experience building reliable ingestion systems with large-scale backfills, incremental processing, checkpointing, rate-limit handling, idempotency, and failure recovery.
Strong understanding of streaming and event-driven architectures, including Kafka, CDC, and micro-batching.
Experience with data orchestration platforms such as Dagster and designing for freshness, partitioning, and incremental computation.
Experience optimizing PostgreSQL and analytical workloads for performance and cost.
Experience operating production infrastructure on GCP, with familiarity with Terraform, Kubernetes/GKE, Helm, GitOps, and CI/CD.
Strong understanding of observability, reliability engineering, and production operations.

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