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# Data Engineer, Amazon Prime Video Product Analytics

- **Company:** Amazon.com, Inc.
- **Location:** Seattle, WA, United States
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Data Analysis, Data Infrastructure, Extract Transform Load (ETL), Machine Learning, Usage Analysis, Data Management, Software Version Control, Data Pipelines
- **Published:** October 27, 2026
- **Apply:** https://www.careerbuilder.com/job-details/data-engineer-amazon-prime-video-product-analytics-seattle-wa--2d91e1ff-183d-4f0b-8d27-8433a40cdb49

## About the Role

Film, Customer Experience, Cellular Telephone, Mobile Games, Data Science, Artificial Intelligence (AI), Business Intelligence, Customer/Client Research, Data Management, Onboarding

## Description

The Prime Video Product Analytics (PVPA) team is a centralized analytics organization supporting the Prime Video Product organization. We bring together Business Intelligence Engineers, Data Engineers, and Data Scientists to deliver comprehensive data tools and insights that drive product decisions and enhance customer experience. Our work spans the full analytics lifecycle-from data infrastructure and pipeline development to advanced analytics, customer insights modeling, AI-driven solutions, and self-service reporting., We are looking for a highly motivated and experienced Data Engineer II to join the PVPA team. In this role, you will be responsible for building and maintaining the data infrastructure that powers Prime Video"s out-of-app and full-funnel customer journey analytics. You will own critical datasets and pipelines that directly support analytics, experimentation, and machine learning use cases, enabling smarter targeting, personalization, and measurement.

As a Data Engineer on the PVPA team, you will:

 * Design, implement, and maintain scalable and reliable data models and ETL pipelines that span the full customer funnel-including the reporting layer for the out-of-app messaging platform and other PV marketing channel infrastructure.
 * Build and enhance the AI tooling and analytics products to enable self-serve and improved efficiency.
 * Partner with engineering and dependency teams to define data contracts, ensure upstream data reliability, and proactively manage communication and change coordination for data schema updates.
 * Collaborate with Business Intelligence Engineers and Data Scientists to deliver clean, structured data for dashboards, reporting, experimentation, full-funnel attribution, and ML use cases.
 * Monitor pipeline health, resolve job and cluster issues, and improve automation, testing, and observability to increase system resilience.
 * Champion data engineering best practices across the team-including documentation, version control, peer reviews, and operational excellence.