We are looking for an L6 Business Intelligence Engineer (BIE) Manager to lead a team of BIEs supporting Cross Border Science at Amazon. This role sits at the intersection of data engineering, analytics, and applied science — owning the BI infrastructure and insights layer that powers AI-driven decision-making across 50+ Cross Border arcs and 8+ marketplace geographies. The ideal candidate will build and manage a team that delivers production-grade BI pipelines, experimentation frameworks, and business metrics instrumentation supporting science initiatives spanning forecasting, pricing, personalization, search, compliance, and generative AI. You will partner closely with Applied Scientists, Product Managers, and Software Engineers to translate complex, multi-marketplace data into actionable intelligence — defining KPIs, building self-service dashboards, and designing A/B test measurement frameworks that quantify the business impact of tech and AI solutions in production. This is a high-visibility role requiring both technical depth (SQL, Spark, Python, AWS data stack) and leadership maturity to hire, develop, and mentor BIEs while operating as a strategic partner to science and product leadership.
7+ years of business intelligence and analytics experience
5+ years of delivering results managing a business intelligence or analytics team, including employee development and performance management experience
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Experience with data visualization using Tableau, Quicksight, or similar tools
Experience in SQL Server/MySQL, or experience that includes strong analytical skills, attention to detail, and effective communication abilities and experience in Redshift
Experience including, building and maintaining data flows and pipelines
Knowledge of product experimentation (A/B testing)
Experience leveraging LLMs and GenAI tools for analytics workflows — prompt-based data exploration, automated insight generation, natural language to SQL, or AI-assisted reporting
Experience building semantic layers or self-service analytics platforms that democratize data access across technical and non-technical consumers
Familiarity with AI-assisted data quality and anomaly detection — automated monitoring of data freshness, drift, and pipeline health using ML-based alerting
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