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
Experience working directly with business stakeholders to translate between data and business needs
Experience in operational analytics - manufacturing, logistics, field operations, supply chain, or physical infrastructure domains where you've analyzed process efficiency, failure modes, or workforce productivity
Experience with workforce analytics - productivity measurement, skill-gap analysis, labor planning, or efficiency modeling that accounts for varying task complexity and work mix
Experience building composite metrics or indices that combine multiple dimensions into weighted scores (similar to OEE, NPS, or operational health indices)
Experience with multivariate analysis - identifying which factors among many are most strongly associated with performance outcomes
Experience building decision-support tools, recommendation frameworks, or analytical playbooks - structured systems where analysis directly translates to operational action
Familiarity with forecasting methods (time-series decomposition, trend analysis, seasonality modeling) for operational planning
Experience building analytical frameworks adopted across multiple teams - reusable tools and methods that scale beyond individual analyses
Experience with Amazon internal data tools (Redshift, Athena, QuickSight) is a plus for internal candidates
Experience with data center, cloud infrastructure, or hardware operations is valuable but not required - domain knowledge can be learned; analytical rigor cannot
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
Additional Information
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.
Amazon Data · Seattle, WA