Data Engineer, Amazon Prime Video Product Analytics

Job summary

DESCRIPTION Prime Video is changing the way customers watch movies, TV shows, live events, and channels—offering unparalleled choice and convenience across devices such as Fire TV, mobile phones, game consoles, and connected TVs. We deliver world-class viewing experiences to customers in over 240 countries and territories.

Benefits

Qualifications

• We deliver world-class viewing experiences to customers in over 240 countries and territories.
• 3+ years of data engineering experience
• Experience with data modeling, warehousing and building ETL pipelines
• 3+ years of analyzing and interpreting data with Redshift, Oracle, NoSQL etc.
• experience
• Experience with SQL
• PREFERRED
• Experience with non-relational databases / data stores (object storage, document or key-value stores, graph databases, column-family databases)
• Experience with AWS
• and location.
• Amazon also offers comprehensive

Responsibilities

• 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:
• 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.

Skills

Degrees

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