Data Engineer II, Creator Services
Listed on Amazon’s official careers site. Posted August 6, 2026. Check that it is still open and apply there.
Apply on Amazon’s careers site →Job description
Amazon's Creator Services leads creator programs that span media, commerce, and culture. The Creator Services team includes Creator Partnerships, creator content studio Wondery, and physical production facilities Studio126. We connect creators and Amazon's diverse businesses, helping bring big ideas to life across Wondery, Amazon Music, Twitch, Audible, Amazon Games, and beyond. Join us in making Amazon the home for creators.
We are seeking a skilled Data Engineer to join our Data & Tech team in building robust, production-ready systems that drive creator monetization at scale. Operating at the intersection of data and software engineering, you will collaborate with Business Intelligence engineers and cross-functional business stakeholders to standardize and curate datasets that underpin AI-driven initiatives and self-service analytics capabilities.
You will also design and build internal tools and software that automate workflows, improve data accessibility, and accelerate creator monetization outcomes. This role sits within the Creator Services data and tech team, and also supports the Creator Ad Partnerships team.
Key job responsibilities
- Design, build, and maintain production-grade ETL/ELT pipelines that process data at scale with high reliability and performance
- Architect and curate data models, warehouses, and dimensional schemas that serve as the foundation for self-service analytics and AI initiatives
- Drive end-to-end technical ownership of data platform components — from system design and architecture reviews to deployment, monitoring, and incident response
- Leverage AWS services — including Redshift, S3, Glue, Lambda, ECS, DynamoDB, and Bedrock — to build scalable and cost-efficient data platforms
- Partner with BI engineers, program managers, and business stakeholders to translate data needs into technical solutions, while advocating for data engineering best practices across the team
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