The Amazon Devices organization designs, produces and markets Kindle e-readers, Fire Tablets, Fire TV Streaming Media Players and Echo Speakers, and is constantly looking to innovate on behalf of customers with new devices in existing or new categories or improving the customer experience on existing platforms. The Devices Analytics team provides customer engagement support to the broader organization for Kindle, Fire, and Echo products - established and emerging countries, worldwide.
We are looking for an innovative, hands-on and customer-obsessed Data Engineer who can be a strategic partner to the product managers and engineers on the team.
Our projects span multiple organizations and require coordination of experimentation, economic and causal analysis, forecasting, and predictive machine learning tools. You will act as the business-facing subject matter expert for data storage, feature instrumentation, and data privacy, with the responsibility of managing end-to-end execution and delivery across projects.
In this role, you will be a technical expert with massive impact. You work tightly with the research scientist and business intelligence engineers on our team to create the data infrastructure and pipelines to drive our machine learning projects from initial experimentation to production level deployment. Your work will directly impact the success of Amazon's growing Devices business. You will work across diverse science/engineering/business teams. You will work on critical data engineering problems, building unique high quality reliable, accurate, consistent, and architecturally sound data sets that are aligned with our business needs.
Key Performance Areas
* Design, implement and support an analytical data infrastructure
* Managing AWS resources including EC2, EMR, S3, Glue, Redshift, etc.
* Interface with other technology teams to extract, transform, and load data from a wide variety of data sources using SQL and AWS big data technologies
* Explore and learn the latest AWS technologies to provide new capabilities and increase efficiency
* Collaborate with Data Scientists and Business Intelligence Engineers (BIEs) to recognize and help adopt best practices in reporting and analysis: data integrity, test design, analysis, validation, and documentation
* Help continually improve ongoing reporting and analysis processes, automating or simplifying self-service support for customers
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