Business Intelligence Engineer III, ASP Insights

AWS is seeking a Senior Business Intelligence Engineer III to lead analytics strategy for the ASP organization, focused on Partner insights and analytics. This team operates at the intersection of data engineering and AI — building systems where intelligent automation handles the default case and human expertise focuses on exceptions, strategy, and novel problems. Working closely with AWS Partner leadership, the Senior BIE owns the end-to-end analytics architecture: data platforms, governance frameworks, self-service products, and the integration points where AI capabilities plug into the stack. This role defines how the team builds, sets technical standards others follow, and drives the evolution from traditional reporting toward systems that learn and scale. The ideal candidate has deep data engineering fundamentals, has worked with AI/ML in production or near-production contexts, and treats system design and organizational influence as equal parts of the job. Key job responsibilities 1. Owns the architecture and technical roadmap for the team's analytics systems — data platforms, governance layers, self-service products, and the integration patterns that connect them to AI-powered downstream applications. 2. Designs and drives implementation of scalable data platforms: pipeline orchestration, automated quality monitoring, schema management, and infrastructure that supports both traditional BI and AI-native consumption patterns. 3. Architects governance frameworks that maintain data trust at scale: lineage, freshness enforcement, validation pipelines, ownership models, and content lifecycle practices — especially as AI systems become consumers of the team's data. 4. Builds and owns automated analytics systems — report generation, KPI monitoring, anomaly detection, insight delivery — designing for progressive automation where AI handles the routine and humans handle the exceptions. 5. Defines the team's AI tooling strategy: evaluates frameworks, builds shared infrastructure (prompt libraries, evaluation patterns, integration templates), and establishes practices that help the whole team work effectively with AI. 6. Drives cross-functional alignment on data product architecture; influences partner teams on integration patterns, API contracts, and standards for how data products interoperate across the ecosystem. 7. Makes technical decisions with broad impact: data modeling trade-offs, build-vs-buy on capabilities, migration strategies from legacy systems, and cost/performance optimization across the stack. 8. Applies advanced statistical and ML methods within production systems; ensures analytical rigor in automated outputs and designs experimentation frameworks that quantify business impact. 9. Mentors and levels up the team on data engineering craft, system design, governance thinking, and practical AI/ML application — raising the bar for what the team can build and maintain. 10. Communicates complex technical architecture and strategy to senior leadership; writes design documents that drive alignment, presents trade-offs clearly, and translates technical capability into business outcomes. A day in the life You start the morning reviewing a design doc from a teammate proposing a new governance pipeline, leaving comments on trade-offs before their review meeting. Mid-morning you're in a cross-team sync aligning on API contracts for a data product three partner teams consume. After lunch you're heads-down writing the architecture for an automated reporting system that replaces a manual weekly process. Late afternoon you pair with a junior BIE on a data modeling problem, then close out drafting a one-pager for leadership on your recommendation to migrate a legacy platform — framing the cost, risk, and timeline clearly.

The PARK (Partner Analytics & Reporting Knowledge) team within ASP Insights owns the end-to-end analytics architecture for AWS's global partner organization — data platforms,