Sr Business Intelligence Engineer, Amazon Global Data Center Ops Central Insight and Analytics Team

Description

• We are looking for a Senior Business Intelligence Engineer to build the diagnostic analytics layer for Amazon's global data center operations. You will move our analytics capability beyond reporting *what*

Description

• We are looking for a Senior Business Intelligence Engineer to build the diagnostic analytics layer for Amazon's global data center operations. You will move our analytics capability beyond reporting *what* happened to explaining *why* — identifying which factors drive metric deviation, decomposing performance into attributable components, and building the analytical frameworks that enable operational leaders to take the right action. GDCO operates one of the world's largest physical infrastructure fleets — hundreds of data centers across 20+ countries — with thousands of technicians performing hardware repairs, rack installations, and preventive maintenance daily. We have strong descriptive analytics (dashboards, WBR metrics), but has opportunity in terms of explaining root causes, attribute performance gaps to specific factors, or recommend proven corrective actions. This leader will focus on building that diagnostic layer. This is a high-impact, high-autonomy role. You will scope analytical problems, build decomposition frameworks, partner with operational leaders to validate findings, and deliver insights that directly influence resource allocation, process design, and investment decisions at the VP level. You'll work with rich operational data at scale: millions of repair tickets, rack lifecycle events, parts inventory flows, workforce scheduling data, and hardware validation results.

Basic Qualifications

• - 10+ years of performing statistical analysis experience - Expert SQL skills — complex analytical queries across large-scale datasets (multi-system joins, window functions, statistical aggregations across petabyte-scale data) - Strong statistical foundation — regression analysis, statistical process control, hypothesis testing, and metric decomposition applied to real business problems - Experience building automated, reproducible analytical pipelines — scheduled systems that serve ongoing business processes at production quality - Proficiency in Python or R for data manipulation, statistical analysis, and visualization - Demonstrated ability to decompose complex business metrics into attributable components — translating "this metric moved" into "here's why, here's who owns each piece, here's the impact" - Strong written and verbal communication — ability to write diagnostic narratives and present complex analysis clearly to VP-level audiences

Preferred Qualifications