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Airwallex is an AI-native financial operating system serving over 676,000 businesses globally, including McLaren Racing, Qantas, SHEIN, and TikTok. The company operates as a regulated backbone for global payments with 85+ licenses and a presence across North America, Europe, the Middle East, and Asia-Pacific. Co-headquartered in San Francisco and Singapore with 2,300+ employees across 27 offices.
The Risk & Compliance team partners closely with legal, regulatory, financial crime compliance, product, engineering, and commercial teams to help Airwallex grow safely and sustainably. Work spans regulatory reporting, financial crime compliance, enterprise risk management, data & privacy, and more across a fast-evolving global regulatory landscape.
In this role, you will own the analytical layer of Airwallex's regulatory data infrastructure, defining correctness, building systems that prove it, and working at the intersection of data engineering, compliance logic, and product intelligence. You are the go-to analytics partner for Regulatory Compliance and Financial Crime Compliance teams, driving impact across the global footprint.
Key responsibilities include:
- Lead analytics and data design for priority regulatory reports (compliance, financial crime, regulatory returns) from requirement translation through data logic, testing, and go-live
- Partner with Regulatory Compliance, FCC, Finance, DataOps, and Data Engineering to standardize common dimensions (accounts, FX, products, entities) across reports
- Co-design data models and mart-layer logic with Data Engineering as an active voice in how reporting dimensions are structured, tested, and versioned
- Work with Data Engineering to define automated test suites and manual QA playbooks for regulatory submissions
- Conduct deep-dive analyses when anomalies, RFIs, or audit findings arise; quantify impact, identify root causes, and propose remediation
- Shape requirements for a scalable regulatory reporting and risk analytics platform
- Champion best practices in analytical rigor, documentation, and validation; provide informal mentorship to analysts and data scientists in adjacent teams
Requirements:
- Bachelor's degree in Mathematics, Statistics, Operations Research, Finance, Economics, or related quantitative discipline
- 5+ years hands-on experience extracting and manipulating large data with SQL; experience with scripting in Python, Shell, or R
- 5+ years proven risk management experience or equivalent, including analytics, modeling implementation, and stakeholder management
- Familiarity with analytics engineering practices: writing modular, tested, documented SQL transformations (e.g., dbt or equivalent); thinking in layers (source → staging → marts)
- Experience building exploratory and production-grade analytics using modern data tools (e.g., Hex, Evidence, or notebook-style environments where SQL, Python, and visualization coexist)
- Excellent communication and stakeholder-management skills; able to explain technical data topics to non-technical audiences, document assumptions clearly, and influence cross-functional partners across Legal, Risk, Compliance, Finance, and Engineering
Preferred qualifications:
- Direct experience working on regulatory reporting for risk and compliance domain
- Hands-on experience with dbt (models, tests, docs, macros) and a modern data warehouse (BigQuery, Databricks, or Snowflake); able to own a data model end-to-end
- Understanding of data pipeline design; knows when a problem belongs in transformation layer vs. application layer vs. reporting layer
- Exposure to model risk or AI/ML governance (e.g., model inventories, validation, monitoring) and interest in how these frameworks intersect with regulatory reporting and compliance analytics
- Experience working with cross-functional, geographically distributed teams; managing by influence is a plus
