Data Engineer, Amazon Global Selling - AIT

Amazon is hiring a Data Engineer, Amazon Global Selling - AIT in Shanghai, China. Level rates it ; you can apply on Amazon.

Level's read on this job

Works on AI ●●●○ is how Level rates this data engineer role: the daily work is on AI products, without building the model.
Tools and skills the posting cites include SQL, ETL and Python.
Level classifies it as a mid-level role.
Level lists 20,000+ other open roles at Amazon.

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.

How we rate this

Data Engineer, Amazon Global Selling - AIT at Amazon rates 65 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.

Classification

Works on AI. The daily work is on AI products, without building the model.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Key facts

Data Engineer, Amazon Global Selling - AIT is an open job at Amazon.
Level rates this role AI Level 3 of 4: Works on AI.
The role is based in CN, 31, Shanghai.
The job was posted on September 22, 2026.
Seniority: mid.
Applications are handled by Amazon. Apply on Amazon

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

Data EngineeringData ModelingData WarehousingFeature EngineeringSQLETLPython

Also common in data engineer jobs at this level

AWS Databricks Machine learning Azure Airflow Generative AI

Pay

Typical data engineer job: $145k

This posting doesn't list pay. Use the typical data engineer figure when pay comes up. See data engineer jobs

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.

Adapt your resume

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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