Sr. Business Intelligence Engineer, Amazon Publisher Monetization, Analytics

Amazon Amazon · Big Tech · NY +1 · Business Intelligence

This role focuses on building AI-powered analytics experiences, specifically conversational agents and prompt-driven interfaces using LLMs and generative AI, to provide proactive insights for Amazon's advertising supported publishers. The engineer will own the end-to-end insight lifecycle, from data modeling to executive recommendations, and mentor other engineers.

What you'd actually do

  1. Own end-to-end business intelligence for Live Events, from data modeling through executive-ready insights
  2. Build deep monetization domain expertise to proactively surface opportunities the business isn't yet asking about
  3. Partner with Product, Business Development, and Executive leadership to translate data into go-to-market strategies and publisher actions with measurable business impact
  4. Conduct deep-dive analyses of business problems, formulate recommendations, and present to senior leadership
  5. Build and architect AI-powered analytics experiences (conversational agents, prompt-driven interfaces) using LLMs and generative AI

Skills

Required

  • 5+ years of professional or military experience
  • 5+ years of SQL experience
  • Experience programming to extract, transform and clean large (multi-TB) data sets
  • Experience with AWS technologies
  • Experience in scripting for automation (e.g. Python) and advanced SQL skills
  • Experience with theory and practice of information retrieval, data science, machine learning and data mining
  • Experience with AI/ML technologies

Nice to have

  • Experience working directly with business stakeholders to translate between data and business needs
  • Experience managing, analyzing and communicating results to senior leadership
  • Experience with data visualization using Tableau, Quicksight, or similar tools
  • Experience with AWS services including S3, Redshift, Sagemaker, EMR, Kinesis, Lambda, and EC2
  • Knowledge of digital advertising or search advertising or analytics solutions (e.g. Google Analytics)

What the JD emphasized

  • AI-powered analytics experiences
  • conversational agents
  • prompt-driven interfaces
  • LLMs and generative AI

Other signals

  • Leverage LLMs and generative AI to build conversational analytics experiences
  • architect self-service data tools
  • move the team from reactive reporting toward proactive, AI-powered intelligence
  • Build and architect AI-powered analytics experiences (conversational agents, prompt-driven interfaces) using LLMs and generative AI