Senior Data Engineer

Airwallex (airwallex.com) is a rapidly growing fintech unicorn, founded in 2015 and now flourishing at the Series E financing stage. With a global footprint across Asia-Pacific, Europe, North America, and APAC, we specialize in providing diverse financial payment solutions for B2B merchants, empowering businesses to operate seamlessly anywhere, anytime.

Who We Are About the Team:

As a global team, we span across Australia, China, USA, and Singapore, revolutionizing applied data science, data engineering and platform solutions to support Airwallex's rapid growth. You will collaborate with a diverse range of cross-functional partners, including Product, Engineering, Marketing, Sales, Finance, and more, to tackle complex data problems and shape the future of fintech.

What you'll do

Data Modeling

• Design and implement robust and scalable data models that support business intelligence, machine learning, and operational needs.

• Possess a deep understanding of data schemas and be able to select appropriate schema designs (e.g., star schema, snowflake, normalized vs denormalized) based on use cases.

• Collaborate closely with business teams to translate their data needs into clean, structured, and well-documented models.

• Understand and promote the concept of SSOT (Single Source of Truth) throughout the data layers and pipelines.

• Maintain data consistency, traceability, and quality across multiple data sources and domains.

ETL and Data Pipeline Management

• Experience building and maintaining both batch and streaming ETL pipelines, with a strong understanding of end-to-end data workflow - from data ingestion to transformation and delivery.

• Able to work closely with Data Platform Engineers (DPEs) and Product Managers (PMs) to quickly identify root causes of data issues and provide efficient, scalable solutions.

• Bonus if you've worked with data across distributed or multi-datacenter systems, including solving challenges related to data migration, duplication, and consistency.

Data Governance

• Participate in and contribute to data governance strategies, policies, and standards.

• Be familiar with any of the six key pillars of traditional data governance (e.g., data quality, data stewardship, metadata management, master data management, data privacy/security, data lifecycle).

Data + AI

• Basic understanding of AI and enjoy thinking about how data engineering and AI can work together in practical and creative ways.

Who you are

We're looking for people who meet the minimum qualifications for this role. The preferred qualifications are great to have, but are not mandatory.

Minimum qualifications:

• Bachelor's degree in Computer Science, Information Systems, Finance, Maths or a related field. A master's degree is a plus.

• Proven experience (5+ years) in designing and implementing ETL processes using tools such as Informatica, Talend, Apache NiFi, or similar. If you have less than five years of work experience but have done very well in similar roles, we're happy to consider you for a Mid-Level position (Data Engineer II). Just keep in mind that this role requires at least two years of work experience.

• Strong expertise in data modeling.

• Proficiency in SQL, database management systems (e.g., MySQL, PostgreSQL, Oracle), and data warehousing solutions.

• Familiarity with Google Cloud Platform (GCP), specifically BigQuery and Airflow.

• Excellent problem-solving skills, with a keen attention to detail and a commitment to producing high-quality work.

• Strong communication and collaboration skills, with the ability to work effectively in a fast-paced, team-oriented environment.

• Outstanding verbal communication skills for effective interaction with overseas teams.

Preferred qualifications:

• Experience with financial industries (or financial applications, with a focus on Treasury and Ledger systems), payment systems, or fintech platforms.

• Knowledge of data governance practices and regulatory requirements in the financial industry.

• Experience with scripting languages (e.g., Python, R) for data analysis and automation.

• Certification in data management or related technologies is a plus.