Amazon Global Selling has been helping individuals businesses increase sales reach new customers around the globe. Today, more than 50% of Amazons total unit sales come from third-party selection. The Global Selling team in China is responsible frecruiting local businesses to sell on Amazon’s 19+ overseas marketplaces supporting local Sellers’ success growth on the Amazon. Our vision is to be the first choice fall types of Chinese business to go globally.
The Amazon Global Selling Analytics, Intelligence, Technology (AGS-AIT) team serves as the research, automation, 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, artificial intelligence tools.
The AGS-AIT team is positioned to establish AI-ready foundational capabilities across the AGSganization while maintaining excellence in business insight generation, self-service BI/AI application development.
AGS-AIT is looking fa Data Engineer to collaborate with cross-functional teams to design develop data infrastructure analytics capabilities fAGS AI Automation initiatives.
Key job responsibilities
? Design implement end-to-end data pipelines (ETL) to ensure efficient data collection, cleansing, transformation, storage, supporting both real-time offline analytics needs.
? Develop automated data monitoring tools interactive dashboards to enhance business teams’ insights 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), build a unified data layer.
? Establish data standardization governance policies to ensure consistency, accuracy, compliance.
? Provide structured data inputs fAI model training inference (e.g., LLM applications, recommendation systems), optimizing feature engineering workflows.
Basic qualifications
1+ years of data engineering experience
Experience with data modeling, warehousing building ETL pipelines
Experience with one more query language (e.g., SQL, PL/SQL, DDL, MDX, HiveQL, SparkSQL, Scala)
Experience with one 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.
Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, IAM roles permissions
更新于 2026-01-26
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1、Design, build maintain batch real-time data systems pipelines
2、Develop ETL (extract, transform, load) processes to help extract manipulate data from multiple sources
3、Maintain optimize the data infrastructure required faccurate extraction, transformation, loading of data from a wide variety of data sources
Automate data workflows such as data ingestion, aggregation, ETL processing
4、Prepare raw data in OLAP databases a consumable dataset fboth technical non-technical stakeholders
5、Partner with data scientists functional leaders in different business units to deploy machine learning models
6、Ensure data accuracy, integrity, privacy, security, compliance through quality control procedures
7、Monitdata systems performance implement optimization strategies
Leverage data controls to maintain data privacy, security, compliance, quality fallocated areas of ownership
Required Knowledge Skills
1、Advanced SQL skills & experience in relational databases database design
2、Strong proficiency in data pipeline workflow management
3、Experience building deploying machine learning models
4、Experience working with Kubernetes container platform
5、Great numerical, analytical problem-solving skills
6、Excellent communicationganizational skills
7、Proven ability to work independently with a team
IT Knowledge Applications
1、Experience working with data streaming related platforms (e.g., Apache Kafka, ksqlDB, Apache Pino, etc.)
2、Experience working with large data sets distributed computing (e.g., Hadoop/Spark, Presto/Trino, Superset, etc.)
3、Proficiency in programming languages, e.g., Python, Java, Go, etc.
更新于 2026-01-13
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