Data Engineer , Worldwide Global Selling -AIT
Confirmed on the employer's own hiring board on Oct 14, 2026. First seen by Alion on Oct 14, 2026. Amazon scores A on the Alion truth index.
Worldwide 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 Worldwide Global Selling Analytics, Intelligence, and Technology (WWGS-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.
AIT is building the next generation of AI-ready data capabilities across the WWGS organization. We are looking for a Senior AI Agent & Data Engineer who can bridge modern data engineering with AI agent development - designing the data foundation that powers our AI applications while directly engineering agentic systems that drive seller intelligence and automation.
Key job responsibilities
- Design and implement end-to-end data pipelines (batch and streaming) for data collection, transformation, and storage - supporting both AI application and analytics use cases
- Build and maintain integration layer data models that serve as a unified, AI-ready data foundation across WWGS domains
- Develop automated data quality monitoring, alerting, and observability tooling to ensure pipeline reliability and data trustworthiness
- Integrate multi-source data (seller behavior, transaction logs, off-platform signals, AI outputs) into a coherent, governed data layer
- Establish data standardization and governance policies ensuring consistency, accuracy, and compliance across AI and BI consumption layers
- 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.
- 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.
- Provide structured data inputs for AI model training and inference (e.g., LLM applications, recommendation systems), optimizing feature engineering workflows. About the team The Worldwide Global Selling Analytics, Intelligence, and Technology (WWGS-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. WWGS-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.
Basic qualifications
- 3+ years of data engineering experience
- Experience with data modeling, warehousing and building ETL pipelines
Preferred qualifications
- Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions
- Experience with non-relational databases / data stores (object storage, document or key-value stores, graph databases, column-family databases)
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