Business Intelligence Engineer, AIT
6 days ago
The Amazon Global Selling Analytics, Intelligence, and Technology (AGS-AIT) team serves as the research, automation, and insight arm of the International Seller Service data hub, enabling rapid delivery of growth insights through strategic investments in regional data foundations, self-service business intelligence solutions, and artificial intelligence tools.
The AGS-AIT team is positioned to establish AI-ready foundational capabilities across the AGS organization while maintaining excellence in business insight generation, and self-service BI/AI application development.
AGS-AIT is looking for a Business Intelligence Engineer to collaborate with cross-functional teams to design and develop data infrastructure and analytics capabilities for AGS AI and Automation initiatives.
Key job responsibilities
- Design, develop, and maintain scalable data infrastructure and ETL pipelines to support business intelligence initiatives using automated dashboard solutions
- Partner with Product, Technical, Business, and Marketing teams to understand data requirements and design scalable and user-friendly BI solutions
- Design and implement AI/LLM-powered solutions to automate human-in-the-loop tasks
- Design and develop statistical analysis to support business decision making using the AI infrastructure and company wide solutions
Basic Qualifications:
- 4+ 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 using SQL to pull data from a database or data warehouse and scripting experience (Python) to process data for modeling
- Hands-on Python experience to process data for modeling, and to automate workflows.
Preferred Qualifications:
- 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
- Hands-on experience implementing LLM-based solutions
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