Amazon Business Intelligence Engineer Alexa 

United States, Massachusetts, Boston 

497874439

The Business Intelligence Engineer:- Will influence big data solutions/access to data set(s) in team architecture and will be solely responsible for the efficient, secure, and performant queries underlying our analytics suite. As such, attention to detail, data integrity, and strong analytical skills (i.e., understanding business implications of what the data says) are required.
- Has knowledge of engineering and operational excellence best practices. Can make enhancements that improve data processes (e.g., data auditing solutions, management of manually maintained tables, automating, ad-hoc or manual operation steps).
- Understands how to make appropriate data trade-offs. Can balance customer requirements with technology requirements. Knows when to re-use code. Is judicious about introducing dependencies.
- Delivers pragmatic solutions. You do things with the proper level of complexity the first time (or at least minimize incidental complexity).
- Understands how to be efficient with resource usage (e.g., system hardware, data storage, query optimization, AWS infrastructure etc.)
- Should have demonstrated success in an environment which offers ambiguously defined problems, big challenges, and quick changes.Key job responsibilities- Work with product managers to define technical requirements for delivering Alexa analytics products like root cause of failure, attribution of error, time series, etc. analyses- Implement and maintain big data operational excellence best practices (alarming, scaling, etc.)
- Execute data backfills (i.e., replacing look back data if new ML model metrics are produced)

- 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 writing complex SQL queries