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

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 - 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

- 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 - 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.