Are you passionate about leveraging data to deliver actionable insight that impacts daily business decision of Amazon? Does the prospect of dealing with massive volume of data excite you? Do you want to build a cutting-edge highly scalable analytics platform using AWS technologies?
The Hardlines Business Intelligence team is looking for an experienced, self-driven, analytical, and strategic Data Engineer. In this role, you will be working in one of the world's largest and most complex data warehouse environments. You should be passionate about working with huge datasets and be someone who loves to bring data together to answer business questions. You should have deep expertise in creation and management of datasets and the proven ability to translate the data into meaningful insights through collaboration with Business Intelligence Engineers (BIEs), Data Scientists and Business Users. In this role, you will have ownership of end-to-end development of data engineering solutions to complex questions, and the Redshift fleets on which the data will be hosted.
The right candidate will possess excellent business and communication skills, be able to work with business owners to develop and define key business questions, and be able to collaborate with BIEs and Data Scientists to analyze data that will answer those questions. You should have a solid understanding of how to build efficient and scalable data infrastructure and data models.
* In this role, you will have the opportunity to display your skills in the following areas:
* Design, implement, and support an analytical data infrastructure providing ad hoc access to large datasets and computing power
* Managing AWS resources including EC2, RDS, Redshift, et cetera
* 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 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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