Amazon Logistics is looking for a smart and ambitious individual to develop and support Amazon Logistics business intelligence and operations reporting and management systems. Amazon Logistics is a large, rapidly growing package delivery operation, that handles packages for Amazon and our customers with worldwide operations.
Do you want to be in the forefront of engineering big data solutions that takes Transportation models to the next generation? Do you have a solid analytical thinking, metrics driven decision making and want to solve problems with solutions that will meet the growing worldwide need? We are looking for top notch Data Engineers to be part of our world class Transportation Business Intelligence team.
The ideal candidate relishes working with large volumes of data, enjoys the challenge of highly complex technical contexts, and, above all else, is passionate about data and analytics. He/she is an expert with data modeling, ETL design and business intelligence tools and passionately partners with the business to identify strategic opportunities where improvements in data infrastructure creates out-sized business impact. He/she is a self-starter, comfortable with ambiguity, able to think big (while paying careful attention to detail), and enjoys working in a fast-paced and global team. It's a big ask, and we're excited to talk to those up to the challenge!
* Experience performing quantitative analysis using complex data sources.
* Hands-on experience on Big data technologies and frameworks. Hive, Spark, Hadoop, SQL on Big Data, Redshift
* Experience in near real time analytics
* Experience with scripting languages i.e. Python, Perl, etc.
* Experience with ETL, Data Modeling, and working with large-scale datasets. Extremely proficient in writing performant SQL working with large data volumes
* Ability to manage competing priorities simultaneously and drive projects to completion.
* Bachelor's degree or higher in a quantitative/technical field (e.g. Computer Science, Statistics, Engineering).
* Experience with large scale data processing, data structure optimization and scalability of algorithms a plus
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