Business Intelligence Engineer Ii, Scot-fo, Tvp

Amazon Amazon · Big Tech · Austin, TX · Business Intelligence

This Business Intelligence Engineer role focuses on building analytic and reporting capabilities to optimize delivery promises within Amazon's supply chain. The role involves collaborating with science and engineering teams, defining and maintaining reports, analyzing historical data, mining data from various sources, and driving data quality improvements. It requires strong analytical, problem-solving, and data mining skills, with experience in SQL, ETL, data visualization, and statistical analysis.

What you'd actually do

  1. Collaborate with software development teams to implement analytics systems and data structures to support large-scale data analysis and delivery of machine learning and econometric models
  2. Define, develop and maintain critical business and operational reports reviewed on a weekly, monthly, quarterly, and annual basis
  3. Analyze historical data to identify trends and support decision making, including written and verbal presentation of results and recommendations
  4. Mine and manipulate data from database tables, simulation results, and log files
  5. Identify data needs and drive data quality improvement projects

Skills

Required

  • 3+ years of analyzing and interpreting data with Redshift, Oracle, NoSQL etc. experience
  • 1+ years of SQL, ETL or Oracle experience
  • 1+ years of processing large, multi-dimensional datasets from multiple sources experience
  • 1+ years of performing statistical analysis experience
  • 1+ years of developing automated reporting 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

Nice to have

  • customer obsessed
  • flexible
  • collaborative team players
  • working across functions and organizations
  • innovative thinking
  • desire to learn

What the JD emphasized

  • machine learning and econometric models
  • large-scale data analysis
  • critical business insight
  • large data sets
  • data quality improvement projects