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Airwallex is an AI-native financial operating system serving over 675,000 businesses globally, including McLaren Racing, Qantas, SHEIN, and TikTok. The company provides regulated financial infrastructure spanning North America, Europe, the Middle East, and Asia-Pacific, with 2,300+ employees across 27 offices.
The Strategic Data team builds data foundations supporting business intelligence, machine learning, and operational decision-making. As a Senior Data Engineer, you will design and implement robust, scalable data models and pipelines that improve how teams use data across the business.
Key responsibilities include:
- Design and implement scalable data models supporting BI, ML, and operational needs, selecting appropriate schema designs (star schema, snowflake, normalized, denormalized) based on use cases
- Partner with business teams to translate data needs into clean, structured, well-documented models while promoting a single source of truth
- Build and maintain batch and streaming ETL pipelines across the full data workflow from ingestion through delivery
- Maintain data consistency, traceability, and quality across multiple data sources and domains, including distributed or multi-datacenter environments
- Contribute to data governance strategies, policies, and standards covering data quality, stewardship, metadata, master data, privacy, security, and lifecycle management
- Collaborate with Data Platform Engineers and Product Managers to identify root causes of data issues and deliver scalable solutions
- Explore practical applications of data engineering and AI across products and operations
This role is based in Seattle with an expectation to work from the office three days per week.
REQUIREMENTS:
Essentials:
- Bachelor's degree or higher in Computer Science, Information Systems, Finance, Mathematics, or related field
- 5+ years of experience designing and implementing ETL pipelines using tools such as Informatica, Talend, Apache NiFi, or similar data integration platforms
- Strong experience with data modeling, data schemas, ETL, and end-to-end data workflows
- Proficiency in SQL, database management systems (MySQL, PostgreSQL, Oracle), and data warehousing solutions
- Familiarity with Google Cloud Platform, particularly BigQuery and Airflow
- Excellent problem-solving skills, attention to detail, and commitment to high-quality work
- Strong communication and collaboration skills, including ability to work with globally distributed teams
- Strength in at least one core area of data engineering (particularly data modeling or ETL) with ability to contribute across the broader discipline
Preferred:
- Experience in financial services, payments, or fintech
- Knowledge of data governance practices and regulatory requirements in the financial industry
- Experience with scripting languages such as Python or R for data analysis and automation
- Experience working with distributed or multi-datacenter data systems, including data migration, duplication, and consistency challenges
- Certification in data management or related technologies
