Data Engineer Amazon
Seattle, WA

Job Description

Want to change the way people watch TV? We are seeking an experienced Data Engineer in Seattle to develop innovative digital video business opportunities for customers.

About Prime Video Channels

Prime Video is fast becoming one of the major global players in digital entertainment. We are changing the way millions of customers enjoy movies and TV shows through Amazon Prime, new releases for rent or purchase, and subscriptions through Prime Video Channels. Prime Video Channels, launched in 2015, is an OTT platform that supports distributing video channels including HBO, Showtime and Starz without a cable subscription on the Prime Video platform.

As a Data Engineer in Prime Video, you will work directly with stakeholders and technical partners to design and implement cutting edge data solutions that provide actionable insights to the business. You will be leading the charge in making granular event data easily usable and accessible, and participate in developing the technical strategy to do so. You will work with a wide range of data technologies (e.g. Kinesis, Spark, Redshift, EMR, Hive, and Tableau) and stay abreast of emerging technologies, investigating and implementing where appropriate.

Our ideal candidate has outstanding technical skills, analytical capabilities, business insight, and communication skills, and maintains a strong passion for technology.

Key Responsibilities include:

* Design, develop, implement, test, document, and operate large-scale, high-volume, high-performance data structures for business intelligence analytics.
* Partner with analysts, applied scientists, data engineers, business intelligence engineers, and software development engineers across Amazon to produce complete data solutions.
* Interface directly with stakeholders, gathering requirements and owning automated end-to-end reporting solutions
* Implement data structures using best practices in data modeling, ETL/ELT processes, and SQL, Oracle, Redshift, and OLAP technologies.
* Gather business and functional requirements and translate these requirements into robust, scalable, operable solutions that work well within the overall data architecture.
* Evaluate and make decisions around dataset implementations designed and proposed by peer data engineers.