Amazon Senior Business Intelligence Engineer WW Ops Finance & 

United States, Washington, Bellevue 

633319367

Key job responsibilities
- Interact with business customers, gather requirements and deliver accurate, easy to use and scalable Business Intelligence solutions in AWS (Redshift, EMR, Glue, S3 etc), Quicksight/Quicksight Q, Python/R, and other Amazon proprietary BI tools
- Build ETL, data pipeline jobs, appropriate aggregations, and automated delivery in SQL, Python/R, shell script.
- Design data models on varied database platforms like Redshift, MySQL or object based data stores.
- Recognize and adopt best practices in reporting and analysis: data integrity, test design, analysis, validation, and documentation.
A day in the life
Within this role you will have unique opportunities to work alongside engineers, product managers, data scientists, program managers and finance leaders to help build next gen analytics and data driven reporting supported via one of the largest finance data warehouses and/or datalake. You will be helping scale the supply chain cost allocation/analytics which spans across multiple geographies of North America and Europe. You will work closely with product managers to design and implement new cost allocation strategies, work with engineers across the transportation network to assess impact of new program rollouts & help build operational and anomaly detection reporting solutions. Amazon has a culture of being builders and you will get to use the latest technology stack offered via AWS and internal amazon tools to help scale big and fast.

- 10+ years of professional or military experience
- 5+ years of SQL experience
- Experience programming to extract, transform and clean large (multi-TB) data sets
- Experience with theory and practice of design of experiments and statistical analysis of results
- Experience with AWS technologies
- Experience in scripting for automation (e.g. Python) and advanced SQL skills.
- Experience with theory and practice of information retrieval, data science, machine learning and data mining