Join the International Seller Services Central Analytics Team at Amazon and contribute to empowering global sellers. Our mission is to build and maintain robust data pipelines that integrate seamlessly with Amazon's internal tools, providing data-driven insights.

We're seeking a skilled and motivated Data Engineer to develop scalable solutions, including extensive data models and complex ETL pipelines for our GenAI-ready data layer. This role offers the opportunity to tackle significant business challenges by creating architecture, designing documents, and building intricate ETL pipelines using AWS services.

Key responsibilities include constructing complex ETL pipelines using Datanet (Redshift) and Craddle (Spark SQL), and managing datasets within AWS DynamoDB, S3, and Redshift. You will develop intricate ETL workflows with AWS Glue and orchestrate step functions using Python or Scala.

This role also involves implementing and enforcing rigorous data quality standards and governance policies, including data validation, cleansing, and lineage tracking. Continuous monitoring and optimization of data pipelines are essential to enhance performance and minimize latency.

Collaboration is key; you'll work closely with data analysts and visualization experts to support data-driven decision-making, including designing and maintaining reporting solutions. Demonstrating profound expertise in AWS data services like S3, Glue, EMR, Redshift, Athena is expected, alongside developing and maintaining mission-critical applications for sellers worldwide. Scripting proficiency in Python or Scala is also required.

The ideal candidate possesses at least 3 years of data engineering experience and 4+ years of SQL proficiency. Experience in data modeling, warehousing, and building ETL pipelines is essential. A Bachelor's degree is required.

Preferred qualifications include hands-on experience with AWS technologies such as Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles. Familiarity with non-relational databases and data stores, including object storage, document or key-value stores, graph databases, and column-family databases, is highly advantageous.