Sponsored Brands (SB) is a self-service advertising program that drives discovery and sales on Amazon. This role is an opportunity to be part of the founding of a global product with the potential for explosive growth, standing at the intersection of e-commerce and advertising. Everyone on the team needs to be entrepreneurial, wear many hats, and work in a highly collaborative environment that's more start-up than big company.
Our team of high caliber analysts, developers and product managers use rigorous quantitative approaches to ensure that we target the right ad to the right Customer managing tradeoffs between monetization, advertiser ROI and Customer relevance. In order to accomplish this we leverage the wealth of Amazon's information to build analyses, models, and set up experiments that ensure that we are thriving to reach global optimums and leverage Amazon's technological infrastructure to display the right ads in real time.
As a Data Engineer in the Sponsored Brands team, you will be working in one of the world's largest and most complex data warehouse environments. You should have extensive experience in the design, creation, management, and business use of extremely large datasets working with Amazon Web Services (AWS) and specifically Redshift Data warehousing solutions. You should have excellent business and communication skills to be able to work with business owners to develop and define key business questions, and to build data sets that answer those questions. Above all you should be passionate about working with huge data sets and someone who loves to bring datasets together to answer business questions and drive change.
* Design, implement and support an AWS analytical data infrastructure providing access to large, complex datasets.
* 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.
* Build robust and scalable data integration (ETL) pipelines using SQL, Python and Spark.
* Collaborate with engineers and scientists to implement advanced analytics algorithms that exploit our rich data sets for statistical analysis, prediction, clustering and machine learning.
* Recognize and adopt best practices in reporting and analysis: data integrity, test design, analysis, validation, and documentation.
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