Data engineer, AIT

Amazon Global Selling has been helping individuals and businesses increase sales and reach new customers around the globe. Today, more than 50% of Amazon's total unit sales come from third-party selection. The Global Selling team in China is responsible for recruiting local businesses to sell on Amazon’s 19+ overseas marketplaces and supporting local Sellers’ success and growth on the Amazon. Our vision is to be the first choice for all types of Chinese business to go globally.
The Amazon Global Selling Analytics, Intelligence, and Technology (AGS-AIT) team serves as the research, automation, and insight arm of the International Seller Service data hub, enabling rapid delivery of growth insights through strategic investments in regional data foundations, self-service business intelligence solutions, and artificial intelligence tools.
The AGS-AIT team is positioned to establish AI-ready foundational capabilities across the AGS organization while maintaining excellence in business insight generation, and self-service BI/AI application development.

AGS-AIT is looking for a Data Engineer to collaborate with cross-functional teams to design and develop data infrastructure and analytics capabilities for AGS AI and Automation initiatives.

Key job responsibilities
• Design and implement end-to-end data pipelines (ETL) to ensure efficient data collection, cleansing, transformation, and storage, supporting both real-time and offline analytics needs.
• Develop automated data monitoring tools and interactive dashboards to enhance business teams’ insights into core metrics (e.g., user behavior, AI model performance).
• Collaborate with cross-functional teams (e.g., Product, Operations, Tech) to align data logic, integrate multi-source data (e.g., user behavior, transaction logs, AI outputs), and build a unified data layer.
• Establish data standardization and governance policies to ensure consistency, accuracy, and compliance.
• Provide structured data inputs for AI model training and inference (e.g., LLM applications, recommendation systems), optimizing feature engineering workflows.