Sr. Machine Learning Data Engineer Amazon
Seattle, WA

Job Description

Passionate about books and data science? Kindle/Books Demand Science and Analytics team is seeking an experienced Sr. Data Engineer to work with ML Scientists, Economists, SDEs and BA/IEs on the next generation of science and ML-based products that will help customers discover and buy more of their favorite books on Amazon (in Kindle, Print, or Audio format).

The Kindle/Books Demand Science and Analytics team owns the development of science-based/ML applications aimed at growing customer engagement through

* shopping and discovery CX (pricing, deals, personalized rewards, and ranking of recommendations and relevant content across multiple surfaces)
* targeted marketing (email, push, on-site, paid media)
* subscription products (including selection and marketing of books for programs like Kindle Unlimited and Prime Reading)

As a Sr. Data Engineer, you will transform billions of daily customer interactions (e.g. browsing, buying, and reading/listening behavior) into highly reliable, quality, and low-latency data structures for analytics, data science, and ML use cases. Ultimately, your work will deepen and accelerate the extent to which we can understand and delight book customers. You will work with a large variety of data sources and will experiment and apply the latest set of big data technologies (ETL and Data Lakes, Redshift, S3, EMR, EC2) to transform data into a better CX with books.

Core Responsibilities

* Use SparkSQL, Redshift, EDX and other big data technologies to build and maintain data infrastructure using software engineering best practices
* Manage AWS resources including EC2, RDS, Redshift, Kinesis, EMR, Lambda etc
* Build and deliver high-quality data architecture and automated pipelines to support data science and ML use cases
* Interface with other technology teams to extract, transform, and load data from a wide variety of data sources
* Continually improve ongoing reporting and analysis processes, automating or simplifying self-service support for customers
* Develop understanding and documentation of data sources and appropriate logic for consumption from various data providers (e.g. search, clickstream, reading, marketing, transactions)
* Enable science and analytics teams to discover and access new data sources

Core Leadership capabilities

* Ownership (think long term, don't sacrifice long-term value for short-term results)
* Invent and simplify (look for new ideas from everywhere, find ways to simplify to implement innovation)
* Insist on high standards (continuously raise the bar on data quality, access, speed, and documentation)
* Deliver results (focus on the key inputs to deliver the high quality-solutions in a timely fashion)