About the position
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. The Customer Relationship and Marketing Tech (CRMT) team is building the next generation of personalized customer engagement. Our mission is to deliver data-driven, automated, and highly personalized marketing experiences that deepen customer relationships and drive growth—across channels like email, push notifications, a.com, WhatsApp, and paid media. CRMT owns the in-house Prime Video outbound messaging platform, which powers end-to-end campaign planning, targeting, and message delivery at global scale. This system supports billions of messages annually, driven by experimentation, audience intelligence, and content personalization. As we expand our impact, we are investing in scalable, reliable data infrastructure to support operational excellence and empower downstream teams with trustworthy, actionable data.
Responsibilities
- Design, implement, and maintain scalable and reliable ETL pipelines for personalization, targeting, marketing analytics, and experimentation.
- Build and evolve data models that support both operational workflows and analytical reporting, optimizing for performance, consistency, and usability.
- Work with Engineering teams to instrument production systems, define data contracts, and implement telemetry for marketing event tracking.
- Support CRMT’s outbound platform by ensuring the availability and accuracy of data required for campaign delivery and measurement.
- Collaborate with dependency teams to ensure upstream data reliability and proactively manage communication and change coordination for data schema updates.
- Partner with Business Intelligence Engineers and Data Scientists to deliver clean, structured data for dashboards, reporting, experimentation, 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.
