Business Intelligent Engineer II, Shipping
6 days ago
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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