Business Intelligence Engineer, Worldwide Defect Elimination Analytics

How often have you had an opportunity to be a member of a team that is tasked with solving customer needs through disruptive and innovative technology? Everyone on the team needs to be entrepreneurial, wear many hats and work in a fast-paced, ambiguous, and highly collaborative environment that's more startup than big company. If this sounds intriguing, then we'd like to talk to you about a role on the Amazon Customer Experience Improvement team. This team drives Amazon towards a defect-free customer experience by building technology that rapidly identifies defects, associates them with the information required to resolve the root cause, and prioritizes the multitude of improvement opportunities based on business and customer needs. To continue expanding our defect elimination program, we seek a passionate, results-oriented, Business Intelligence Engineer (BIE).

The Business Intelligence Engineer will partner with Software Developers, Product Managers, Applied Scientists, and Program Managers to surface insights on customer issues, define key performance indicators for our products, and build robust measurement mechanisms to track our progress against goals. The ideal candidate has strong business judgment, organization skills, backbone, experience measuring product performance, and collaborates well with with product owners to answer key questions. To thrive, you must be detail oriented, enthusiastic and flexible; in return you will gain tremendous experience with the latest in data technologies, working alongside a talented, cross-functional team on problems that directly shape the customer experience at scale.

Key job responsibilities

• Design, develop and maintain scaled, automated, user-friendly systems, reports, and dashboards.
• Partner with operations/business teams, economists, and ML teams to consult on, develop, and implement KPIs, automated reporting/process solutions, and data infrastructure improvements that meet business needs.
• Design data architectures that enable agentic workflows: structured data access layers, tool-use APIs, context management systems consumed autonomously by AI agents, and self-serve analytics.
• Develop end-to-end automation of pipelines for applications and ML models, including data infrastructure for AI agent systems.
• Apply analytical skill to extract meaningful insights from large, complex data sets, including unstructured text corpora used in generative AI applications.
• Serve as liaison between business and technical teams to achieve project objectives including data gathering, problem solving, modeling, and communicating insights and recommendations.
• Leverage LLM-based agents to automate routine analytics work like query generation, data validation, anomaly detection, and report narration.
• Stay current on advances in AI/ML data infrastructure (e.g., feature stores, vector search, streaming inference pipelines) and evaluate applicability to defect elimination use cases.