Business Intelligence Engineer, Amazon Freight

Job description

Are you looking to build and scale a new and growing business with Amazon? If so, you might be interested in this role with Amazon Freight.

Amazon Freight is a startup business within Amazon focused on developing an industry-leading transportation offering to provide external freight Customers access to the world-class network and freight services currently used to facilitate Amazon’s own freight movements. Our freight offerings/portfolio ensures our Customer’s freight is moved with the highest level of service and quality at a competitive price across multiple modes of transportation, including truckload, less-than-truckload, and intermodal.

In doing this, Amazon is deeply studying customer shipping needs and building innovative, market-leading solutions that will organically attract customers towards our services.

We are seeking a Business Intelligence Engineer who is passionate about leveraging data to deliver actionable insights for Amazon Freight. Our ideal candidate is someone who has strong technical and analytical skills, enjoys building a new business and the ambiguity that comes with that. You should thrive and succeed in an entrepreneurial environment and won't be hindered by ambiguity. If you enjoy innovating, thinking big and want to contribute directly to the success of an industry changing business, you may be a prime candidate for this position.

Key job responsibilities

· Generate insightful analysis, reporting, and data visualizations to answer critical business questions, proactively identify notable trends, and communicate business performance

· Translate data into actionable insights for stakeholders while proactively identifying new areas of opportunity to drive further improvements

· Apply expertise in data mining and modeling leveraging multiple data sources to uncover unique insights, identify key performance drivers, and build predictive models

· Work backwards from your end-customer to define, design, and execute each work item with an emphasis on improved metrics, productivity, and efficiency

· Recognize and adopt best practices in reporting and analysis: data integrity, test design, analysis, validation, and documentation