Principal Business Intelligence Engineer, Amazon Customer Service
Description
We are looking for a Principal Business Intelligence Engineer to set the technical vision for how CXP creates value from data. You will architect the data systems and governance frameworks that underpin a VP level organization, define what "data as a product" means in practice for CXP, and tackle the hardest analytical challenges head on: defining who our customer is, mapping the full customer journey across channels, building the data foundations for personalized support models, ensuring metric integrity at scale, and designing the integration patterns that connect traditional BI to GenAI and ML powered automation. You will influence across science, product, and engineering teams, mentor a team of BIEs, and present strategy directly to senior leadership. If you want to shape how one of the world's largest customer service organizations understands its customers and transforms every interaction, this is the role.
Key job responsibilities
- Define the customer and map the journey. Build the data models and analytical frameworks that give CXP a unified view of the customer journey across every channel (chat, voice, home page).
- Build the data foundation for personalized support. Own the curated data products (customer profiles, interaction histories, intent signals, resolution patterns) that enable CXP to tailor the experience based on customer context.
- Own the technical architecture and roadmap for CXP's analytics data ecosystem, including pipeline orchestration, schema design, data quality monitoring, and the integration layer feeding BI and AI/ML systems.
- Operationalize "data as a product" with clear ownership, freshness SLAs, lineage, and governance standards that scale across multiple director organizations.
- Drive metric integrity by establishing canonical definitions, reconciling data sources, and building validation frameworks leadership can trust.
- Lead cross functional alignment with Science, Product, and Engineering teams on data modeling standards and integration patterns.
- Make high judgment technical decisions on modeling trade offs, build vs. buy, and cost/performance optimization.
- Mentor and raise the bar for a team of BIEs in analytics best practices, governance, and AI/ML application.
- Communicate strategy to Director and VP level leadership through narratives and design documents.