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.

• About the team
• 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, governance frameworks, self-service products, and the AI integration layer that connects them. We serve thousands of stakeholders across partner sales, operations, and leadership, and we're actively evolving from traditional BI toward systems where intelligent automation handles the default case and human expertise focuses on strategy and exceptions. This role shapes that evolution — setting technical standards, making architecture decisions, and raising the bar for what the team builds.

• About AWS
• Diverse Experiences
• AWS values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.

• Why AWS?
• Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses.

• Inclusive Team Culture
• Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness.

• Mentorship & Career Growth
• We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.

• Work/Life Balance
• We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud.

AWS Sales, Marketing, and Global Services (SMGS) is responsible for driving revenue, adoption, and growth from the largest and fastest growing small- and mid-market accounts to enterprise-level customers including public sector. The AWS Global Support team interacts with leading companies and believes that world-class support is critical to customer success. AWS Support also partners with a global list of customers that are building mission-critical applications on top of AWS services.

Basic qualifications

• - 5+ years of SQL experience
• - Bachelor's degree or foreign equivalent in Computer Science, Engineering, Mathematics, Statistics, Economics, or a related field
• - Experience in scripting for automation (e.g. Python) and advanced SQL skills.
• - Experience architecting and owning end-to-end data platforms serving multiple downstream consumers
• - Experience with cloud-based data infrastructure (Redshift, Athena, Spark, or equivalent) including performance optimization and cost management

Preferred qualifications

• - Experience building or integrating AI/ML capabilities into data platforms or analytics products (LLMs, RAG, agent frameworks, knowledge graphs, or similar)
• - Experience designing governance or quality frameworks for data consumed by automated systems
• - Experience defining tooling strategy for a team — evaluating options, building shared infrastructure, and establishing reusable patterns
• - Track record of building automated analytics systems (autonomous reporting, intelligent alerting, self-service interfaces)
• - Experience mentoring engineers and raising team capability across data engineering and AI/ML practices
• - Strong systems thinking — ability to see upstream/downstream impact across a complex data ecosystem
• - Experience in GTM analytics, partner ecosystem data, or sales operations at scale

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