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Staff Data Engineer, Data and AI, Customer Data Platform Staff

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Summary

Staff Data Engineer at Airwallex (Singapore) building the data foundations of the Customer Data Platform: owning scalable batch and streaming ETL pipelines with Databricks, dbt, and Spark, plus SQL, data warehousing, and data modelling on GCP to power analytics, ML, and AI applications.

Staff Data Engineer, Data and AI, Customer Data Platform

Airwallex is the AI-native financial operating system for a real-time, intelligent economy. More than 675,000 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.

Skills, with evidence

Solid understanding of data schemas, including star, snowflake, normalised, and denormalised designs must have

  • SQL Strong proficiency in SQL, database management systems, data warehousing, and data modelling for business intelligence, machine learning, and operational needs must have
  • Spark Own scalable ETL pipelines across batch and real-time workflows, using technologies such as Databricks, DBT, and Spark must have
  • Streaming At least seven years of experience designing and implementing scalable ETL pipelines, including batch and streaming workflows must have
  • Airflow / orchestration Familiarity with Google Cloud Platform and modern data infrastructure tools, including Databricks and Airflow
  • Machine learning You'll build the data foundations that power Airwallex's Customer Data Platform, helping teams turn customer information into trusted, reusable data for analytics, machine learning, operational decision-making, and practical AI applications. not practised here
  • Warehousing - Strong proficiency in SQL, database management systems, data warehousing, and data modelling for business intelligence, machine learning, and operational needs
  • dbt - Own scalable ETL pipelines across batch and real-time workflows, using technologies such as Databricks, DBT, and Spark
  • GCP - Familiarity with Google Cloud Platform and modern data infrastructure tools, including Databricks and Airflow not practised here

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