Business Intelligence Engineer, Amazon Kids+

Amazon Amazon · Big Tech · Seattle, WA · Business Intelligence

This role focuses on building data pipelines and analytics tools for the Amazon Kids+ subscription service, with a specific emphasis on leveraging Generative AI and agentic AI workflows for data analytics and automation. The primary focus is on data engineering (L0) with a secondary focus on agentic AI applications (L4).

What you'd actually do

  1. Own the design, development, and maintenance of datasets, ongoing metrics, analyses and dashboards that provide insights into subscriber behavior, engagement patterns, and content performance
  2. Develop queries and visualizations for standardized reporting around key subscription metrics including acquisition, retention, and engagement
  3. Continually improve ongoing reporting and analysis processes, automating or simplifying self-service support for stakeholders across Product, Content, Marketing, and Leadership teams
  4. Use analytical and statistical rigor to identify performance gaps and opportunities for optimization across the subscriber lifecycle

Skills

Required

  • Analyzing and interpreting data with Redshift, Oracle, NoSQL etc. experience
  • Experience with one or more industry analytics visualization tools (e.g. Excel, Tableau, QuickSight, MicroStrategy, PowerBI)
  • Experience with statistical methods (e.g. t-test, Chi-squared)
  • Experience with scripting language (e.g., Python, Java, or R)
  • Working knowledge of Generative AI (LLMs, agentic AI, context engineering) and its application to data analytics, automation, or BI tooling

Nice to have

  • Master's degree, or Advanced technical degree
  • Demonstrated experience building agentic AI workflows (e.g., multi-step automation, LLM-augmented pipelines) for data modeling, metric generation, or analytical reporting

What the JD emphasized

  • Generative AI (LLMs, agentic AI, context engineering)
  • agentic AI workflows

Other signals

  • Generative AI (LLMs, agentic AI, context engineering) and its application to data analytics, automation, or BI tooling
  • Demonstrated experience building agentic AI workflows (e.g., multi-step automation, LLM-augmented pipelines) for data modeling, metric generation, or analytical reporting