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Amazon BI Engineer Amazon Music Disco & G DISCO Mexico, Mexico City 999906343
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In this role, you will lead the analytics and insights efforts for an Amazon Music Program. You will help build the analytics roadmap and direction for the global roll-out and work with stakeholders and partners to deliver self-service solutions, insights, learning white papers and models to advance our analytics and science offerings. The ideal candidate must be willing to prioritize across multiple stakeholders and projects to identify the best path forward and be ready to jump into a fast-paced, dynamic and fun environment.Key job responsibilities
• Lead analytics and insights efforts for a specific Amazon Music program
• Work as the internal lead analytics consultant for marketing teams and collaborate on long-term strategy together
• Define and deliver reporting and visualization that supports the analytical needs of internal business leaders
• Drive self-service solutions for senior business stakeholders informing marketing investment decisions
• Use analytical insights to help increase business and teams effectiveness against key strategic goals
• Build a thorough understanding of the streaming music industry, the subscription business model, and key internal partners across Prime, Amazon devices, media and entertainment businesses
• Partner with analytics and science teams across Amazon entertainment and subscription businesses
• Educate and evangelize across internal teams on analytics, insights and measurement by writing whitepapers and knowledge documentation and delivering learning sessions
BASIC QUALIFICATIONS
- 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
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