Amazon
Senior BIE, Business Intelligence & Analytics (BIA)
$130K–$176K
Seattle, WA · Senior · Full-time · Posted Oct 23, 2026
- Experience
- 10+ years of professional or military experience; specified 8+ years in SQL, large multidimensional datasets, statistical analysis, and a related occupation.
- Education
- Bachelor's degree or foreign equivalent in Computer Science, Engineering, Mathematics, Statistics, Economics, or a related field.
About the job
Leads development of governed, AI-powered analytics and automation for AWS Platform Experience to support executive decisions and revenue assurance.
What you'll do
- Own the analytics stack roadmap, data models, semantic layers, governance, and AI integration patterns.
- Build AI-driven root-cause investigation, anomaly detection, and proactive insight products; automate recurring reports.
- Set metric governance and technical standards, assess statistical methods and AI outputs, and mentor engineers while aligning stakeholders.
What they ask for
- Bachelor's degree or foreign equivalent in Computer Science, Engineering, Mathematics, Statistics, Economics, or a related field.
- 8+ years of SQL experience; 8+ years processing large, multi-dimensional datasets from multiple sources; and 8+ years performing statistical analysis.
- 8+ years as a Data Analyst, Data Engineer, Business Intelligence Analyst, or related occupation.
- Experience programming to extract, transform, and clean large (multi-TB) datasets; data visualization with Tableau, Quicksight, or similar; AWS technologies; and automation scripting (e.g. Python) with advanced SQL skills.
Nice to have
- Master's degree in a related field.
- Experience with information retrieval, data science, machine learning, and data mining; or as a BIE in a technology company.
Pay
130,400.00 - 176,300.00 USD annually (base salary)
Tools
Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Amazon Redshift, Athena, SQL, Tableau, Quicksight, Python
AI focus
Develops AI-powered analytics for root-cause investigation, anomaly detection, and proactive insights, using LLMs, RAG, and agent frameworks; evaluates AI outputs for accuracy, auditability, and trust.
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