Amazon Music is awash in data To help make sense of it all, the DISCO (Data, Insights, Science & Optimization) team: (i) enables the Consumer Product Tech org make data driven decisions that improve the customer retention, engagement and experience on Amazon Music. We build and maintain automated self-service data solutions, data science models and deep dive difficult questions that provide actionable insights. We also enable measurement, personalization and experimentation by operating key data programs ranging from attribution pipelines, northstar weblabs metrics to causal frameworks. (ii) delivering exceptional Analytics & Science infrastructure for DISCO teams, fostering a data-driven approach to insights and decision making. As platform builders, we are committed to constructing versátil, reliable, and scalable solutions to empower our customers. (iii) accelerates and facilitates content analytics and provides independence to generate valuable insights in a fast, agile, and accurate way.





This domain provides analytical support for the Consumer Product Tech org to make data driven decisions while launching new features and evaluating existing features with the end goal of improving the customer experience.
DISCO team enables repeatable, easy, in depth analysis of music customer behaviors. We reduce the cost in time and effort of analysis, data set building, model building, and user segmentation. Our goal is to empower all teams at Amazon Music to make data driven decisions and effectively measure their results by providing high quality, high availability data, and democratized data access through self-service tools.

If you love the challenges that come with big data then this role is for you. We collect billions of events a day, manage petabyte scale data on Redshift and S3, and develop data pipelines using Spark/Scala EMR, SQL based ETL, Airflow services.

We are looking for talented, enthusiastic, and detail-oriented Data Engineer, who knows how to take on b

📌 Data Engineer (Ciudad de México)
🏢 Amazon
📍 Ciudad de México