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

D8 LooP; Singapore; On-site; SGD 90,000 - 130,000; Full time

Job summary

Airwallex is hiring a Staff Data Engineer in Singapore to build and scale the Customer Data Platform. You will own data pipelines, data models, and governance to enable analytics and AI initiatives across product and marketing teams.

We seek deep experience with SQL, Spark, Databricks, DBT, and GCP; collaboration with data scientists and engineers to convert customer data into trusted, reusable assets for real-time decisions is essential.

Qualifications

• Strong proficiency in SQL and data warehousing for BI/ML.

Skills

SQL

Spark

Streaming

Airflow

GCP

Tools

Databricks

DBT

Job description

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