BI Engineer, Prime Data Engineering and Anaytics

BI Engineer, Prime Data Engineering and Anaytics

Join Amazon's Prime Data Engineering & Analytics (PDEA) team as a Business Intelligence Engineer (BIE) to revolutionize how we understand, measure and serve our global Prime customer base. You'll architect and build enterprise-level data analytics solutions, including scalable ETL pipelines, automated AI powered reporting systems, and self-service analytics platforms that process petabytes of data daily across multiple dimensions. Working with technologies like AWS, Apache Kafka, Spark, and modern AI/ML tools, you'll create data analysis products that power critical decision-making across Amazon.

As a BIE, you'll be instrumental in defining and implementing key business metrics, building efficient dimensional data models, and leveraging advanced AI technologies like Quick Suite and ML-powered analytics. You'll design intuitive self-service platforms that enable business users to independently explore data, discover insights, and create automated reports. Beyond technical implementation, you'll lead end-to-end project execution, manage worldwide Prime core services data infrastructure, and establish key relationships across Amazon's business units.

This role offers the opportunity to provide technical mentorship, drive architectural decisions, and shape the future of Amazon Prime's data ecosystem, directly impacting millions of customers globally.

• 3+ years of analyzing and interpreting data with Redshift, Oracle, No
  
  SQL 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, Dynamo
  
  DB, 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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