Senior Data Engineer

About Airwallex

Airwallex is the only unified payments and financial platform for global businesses. Powered by our unique combination of proprietary infrastructure and software, we empower over 200,000 businesses worldwide - including Brex, Rippling, Navan, Qantas, SHEIN and many more - with fully integrated solutions to manage everything from business accounts, payments, spend management and treasury, to embedded finance at a global scale.

Proudly founded in Melbourne, we have a team of over 2,000 of the brightest and most innovative people in tech across 26 offices around the globe. Valued at US$8 billion and backed by world-leading investors including T. Rowe Price, Visa, Mastercard, Robinhood Ventures, Sequoia, Salesforce Ventures, DST Global, and Lone Pine Capital, Airwallex is leading the charge in building the global payments and financial platform of the future. If you're ready to do the most ambitious work of your career, join us.

Responsibilities

Part 1. 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.

Part 2. 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.

Part 3. 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).

Part 4. Data + AI

• We hope you have a basic understanding of AI and enjoy thinking about how data engineering and AI can work together in practical and creative ways.

These four areas are the main focuses of the DE team. Ideally, you should be strong in at least one of them - especially data modeling or data ETL. If you also have experience or skills in the other areas, that would be a big plus.

Minimum qualifications:

• Bachelor's degree or higher in Computer Science, Information Systems, Finance, Mathematics, or a related field.

• Minimum 5 years of proven experience designing and implementing ETL pipelines, with a strong understanding of the strategies, rules, and processes involved in building scalable and reliable data pipelines.

• Experience with streaming data pipelines or real-time data processing technologies, such as Apache Kafka or Apache Flink.

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

• Familiarity with Google Cloud Platform (GCP) and modern data infrastructure tools, including Databricks and Airflow.

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

• Excellent problem-solving skills with strong 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, with the ability to collaborate effectively with globally distributed teams.

Preferred qualifications:

• Experience with financial industries, 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