Amazon’s global fulfillment network enables any merchant to ship items that are ordered on Amazon to any place on earth. There is a complex network of ways in which items move between vendor locations, Amazon warehouses, and customer locations as well as several intermediate locations through which packages travel before reaching the customer. With a scale of millions of packages, each with different attributes and delivery requirements, what results is a highly dense graph of nodes.

We have built a highly respected BI and Data Engineering team which is focused on solving complex problems in worldwide transportation using workflows, optimization algorithms, and machine learning systems. These are large-scale distributed systems handling millions of packages being shipped through the Amazon logistics network.

As a Business Intelligence Engineer, you will develop datasets, dashboards, reports, and visualizations using BI tools, and provide data-driven recommendations to support strategic planning within Amazon Shipping. Additionally, you will deliver across planned projects that will improve overall cost and profitability for the business.

Key job responsibilities
Data Management and Integration: Collect, process, and manage large datasets from various sources, ensuring data accuracy, consistency, and accessibility.

Data Analysis: Analyze complex data sets to identify trends, patterns, and insights that can inform business decisions.

Reporting and Visualization: Develop and maintain dashboards, reports, and visualizations to present data insights in an understandable and actionable format.

Stakeholder Collaboration: Work closely with stakeholders to understand their data needs, provide data-driven recommendations, and support strategic planning.

Tool and Technology Utilization: Utilize and stay updated with the latest BI tools and technologies to enhance data analysis and reporting capabilities.

- 3+ years of analyzing and interpreting data with Redshift, Oracle, NoSQL etc. experience
- Experience with data visualization using Tableau, Quicksight, or similar tools
- Experience with data modeling, warehousing and building ETL pipelines
- Experience in Statistical Analysis packages such as R, SAS and Matlab
- Experience using SQL to pull data from a database or data warehouse and scripting experience (Python) to process data for modeling

- Experience with AWS solutions such as EC2, DynamoDB, S3, and Redshift
- Experience in data mining, ETL, etc. and using databases in a business environment with large-scale, complex datasets

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