Description

Are you a data engineer eager to make a meaningful impact while developing your technical skills? Join the Seller Fees Tech organization at Amazon and contribute to critical systems that enable millions of entrepreneurs worldwide to build successful businesses on our platform.

As a Data Engineer, you'll architect and implement the data pipelines, analytics frameworks, and reporting systems that power critical operations across Amazon's global marketplaces. Your work will enable data-driven decision making for fee structures, incentive programs, and adjustments. You will build scalable data solutions that process billions of transactions while maintaining high data quality and governance standards.

In this role, you'll leverage your expertise to transform complex financial data into actionable insights that drive marketplace expansion strategies. You'll have the opportunity to pioneer GenAI-powered data solutions that improve analytics efficiency and uncover hidden patterns in the data. Working at the intersection of big data technologies, financial systems, and marketplace economics, you'll solve technical challenges that require both deep data engineering expertise and strong business acumen.

The Seller Fees Tech organization supports over 2MM+ active sellers by ensuring accurate, transparent fee calculations. Your data solutions will provide the analytical foundation for financial decisions that affect seller profitability and marketplace growth worldwide.

Key job responsibilities
1. Design, develop, and maintain automated ETL/ELT pipelines with monitoring using Python, Spark, SQL, and AWS services such as Redshift, S3, Glue, Lambda

2. Optimize data warehouse and data lake architectures using best practices for DDL, physical and logical table design, data partitioning, compression, and parallelization

3. Implement and support reporting and analytics infrastructure for internal business customers

4. Develop optimized data models and transformations to ensure high-quality, well-structured data for business and analytics applications

5. Develop and maintain enterprise-scale data security solutions including data encryption, database user access controls, logging, and permissions management for data warehouse and data lake implementations

6. Maintain data warehouse and data lake metadata, data catalog, and comprehensive user documentation

7. Collaborate with internal business customers and technical teams to gather, document, and implement requirements for data publishing and consumption via data warehouse, data lake, and analytics solutions

8. Stay current with emerging technologies, tools, and trends (including AI advancements), evaluating and incorporating them into the existing data ecosystem for continuous improvement