AmazonPosted 2d ago

Data Engineer, Amazon Global Selling - AIT

Data Engineer, Amazon Global Selling - AIT at Amazon scores 65 out of 100 on AI centrality, which makes it a Level 3 role on this board.

CN, 31, Shanghaimidfull-time

AI in this role

Design and build data infrastructure, ETL pipelines, and data layers to support AI and automation initiatives.

sqletlpython

data-engineeringdata-modelingdata-warehousingfeature-engineering

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.
• Explore innovative AI-data integration use cases (e.g., embedding AI-generated insights into BI tools).
• Provide technical guidance and best practice on data architecture and BI solution

Basic qualifications

- 1+ years of data engineering experience
- Experience with data modeling, warehousing and building ETL pipelines
- Experience with one or more query language (e.g., SQL, PL/SQL, DDL, MDX, HiveQL, SparkSQL, Scala)
- Experience with one or more scripting language (e.g., Python, KornShell)

Preferred qualifications

- Experience with big data technologies such as: Hadoop, Hive, Spark, EMR
- Experience with any ETL tool like, Informatica, ODI, SSIS, BODI, Datastage, etc.

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

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Skills and AI tools this role asks for

Data EngineeringData ModelingData WarehousingFeature EngineeringSqlEtlPython

Questions you could be asked

Tell me about a project where data engineering was part of your work. What did you do?
Tell me about a project where data modeling was part of your work. What did you do?
Tell me about a project where data warehousing was part of your work. What did you do?
Tell me about a project where feature engineering was part of your work. What did you do?
Walk me through how you've used Sql in your day-to-day work.

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List these exact terms on your resume: Data Engineering, Data Modeling, Data Warehousing, Feature Engineering, and Sql. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
Show where AI is part of your daily process, not a one-off project — this role expects it to be a running habit.

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