Amazon's Demand Generation team is seeking exemplary software development engineers with broad technical skills to help us optimize how we synthesize massive amounts of data on the web and in our systems, apply predictive models and apply complex business rules into demand generation engine. As a member of this team you will employ object oriented techniques in Java and leverage strong data modeling and RDBMS skills to help us design and deliver Amazon's next generation of demand generation application. You will research and drive implementations of predictive models, business rule management, messaging, workflow, and web services technologies. Along the way you'll gain and leverage a unique understanding of how Amazon.com works; from CRM software systems that is mission critical for sales operations, to the details of our business model, and the relationships we're establishing with an ever-growing network of merchants who depend on Amazon.com to run their online stores.
The ideal candidate will draw upon exemplary analytical, critical thinking, and problem solving skills, deep software development experience, and a passion for creating maintainable, highly reliable, distributed systems. Successful members of this team collaborate effectively with internal end-users, aggressive cross-functional software development teams, and technical support/sustaining engineering teams to solve problems, implement new demand generation solutions, and deliver successfully against high operational standards of system availability and reliability. We look for engineers who are excellent communicators, self-motivated, flexible, hard working, and who like to have fun.
Qualifications:
- BS degree in Computer Science and 0 to 3 years of industry experience developing large scale distributed software systems
- Solid proficiency with an Object Oriented language (Java or C++)
- Experience with any/all of the following preferred: RDBMS scaling challenges, data modeling, XML processing, SOAP, REST, Web crawling, data mining, machine learning, distributed systems, parallel computing, pattern analysis, predictive modeling
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