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# Business Intel Engineer - I, Amazon Robotics, Amazon Robotics, Global Operations - Artificial Intelligence (GO-AI)

- **Company:** Amazon.com, Inc.
- **Location:** Boston, MA, United States
- **Experience:** Experienced
- **Salary:** $82,300.0 - $144,000.0
- **Contract:** Temporary contract
- **Skills:** Java (Programming Language), Microsoft Excel, Artificial Intelligence, Data Analysis, Data Integrity, Data Visualization, Data Warehousing, Python (Programming Language), MicroStrategy, NoSQL, Oracle (Applications), Power BI, SQL Databases, Tableau (Software), Data Processing, Scripting, Mobile Robots, Apache Spark, AWS Lambda, Data Pipelines, Amazon Redshift
- **Published:** July 8, 2026
- **Apply:** https://www.jobmonkeyjobs.com/career/27831638/Business-Intel-Engineer-I-Amazon-Robotics-Amazon-Robotics-Global-Operations-Artificial-Intelligence-Go-Ai-Massachusetts-Boston-7375

## About the Role

2+ years of analyzing and interpreting data with Redshift, Oracle, NoSQL etc. experience

 - Experience with data visualization using Tableau, Quicksight, or similar tools
 - Experience with one or more industry analytics visualization tools (e.g. Excel, Tableau, QuickSight, MicroStrategy, PowerBI) and statistical methods (e.g. t-test, Chi-squared)
 - Experience with scripting language (e.g., Python, Java, or R)

Preferred Qualifications

 - Master's degree, or Advanced technical degree
 - Knowledge of data modeling and data pipeline design
 - Experience with statistical analysis, co-relation analysis

## Description

* Design, development and ongoing operations of scalable, performant data warehouse (Redshift) tables, data pipelines, reports and dashboards.

 * Development of moderately to highly complex data processing jobs using appropriate technologies (e.g. SQL, Python, Spark, AWS Lambda, etc.)

 * Development of dashboards and reports.

 * Collaborating with stakeholders to understand business domains, requirements, and expectations. Additionally, working with owners of data source systems to gain insights into capabilities and limitations.

 * Deliver minimally to moderately complex data analysis.

 * Adopt best practices in reporting: data integrity, test design, validation, and documentation.

 * Ensure excellence in verbal and written communication.


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