Business Intelligence Engineer III, ASP Insights

About the role

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.

Responsibilities

• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• Applies advanced statistical and ML methods within production systems; ensures analytical rigor in automated outputs and designs experimentation frameworks that quantify business impact.
• 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.
• 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.

Requirements

• 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

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

Skills

• Advanced statistical and ML methods within production systems
• Data modeling trade-offs, build-vs-buy on capabilities, migration strategies from legacy systems, and cost/performance optimization across the stack
• Effective communication and presentation skills
• Ability to work independently and as part of a team

Benefits

• Comprehensive benefits including health insurance, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage
• 401(k) matching
• Paid time off
• Parental leave

Pay

Base salary range for this position is $130,400.00 - $176,300.00 USD annually.

Schedule

Full-time