Are you passionate about turning operational data into the decisions that run a global data center fleet? Do you want to do that work on a platform that pairs traditional BI with the GenAI capabilities that are reshaping the field?

The Central Infrastructure Analytics Team (CIAT) is the unified source for Infrastructure Operations data and business intelligence solutions across AWS's global data center fleet. We support Central Operations leaders running rack install, decommission, repair, logistics, capacity optimization, and network operations.

We are looking for a Business Intelligence Engineer to design and own customer-facing analytics products and to drive analytics adoption across our customer teams. You will partner with operations leaders to translate business questions into measurable KPIs, build dashboards and metric layers in QuickSight, and increasingly leverage GenAI tools — Amazon Q, natural language query interfaces, and retrieval-augmented analytics — to multiply your impact across thousands of users.

Successful BIEs on this team are customer-obsessed, comfortable with operational ambiguity, and equally interested in building great dashboards and in teaching customers to build their own. They write strong SQL, communicate clearly with non-technical leaders, and treat GenAI as a tool they actively use rather than a topic to read about.

Key job responsibilities

• Design, build, and maintain dashboards and analytical reporting solutions in QuickSight that support InfraOps decision-making
• Partner with business and technical stakeholders to translate operational questions into KPIs, data products, and Weekly and Monthly Business Review (WBR/MBR) narratives
• Develop and present recommendations to senior leaders, including written narratives and verbal walk-throughs of insights
• Design Amazon Q topics and supporting datasets that enable customer analysts to self-serve on questions previously requiring a CIAT engagement
• Use CIAT's GenAI tools — Amazon Q, natural language query interfaces, and retrieval-augmented analytics — as a primary part of the job
• Contribute to CIAT's analytics enablement programs that train Central Ops analysts on BI tools, query platforms, and GenAI capabilities
• Write SQL against the team's data warehouse and datalake to validate metrics, investigate data quality issues, and prototype analytics

A day in the life
Most days mix a few hours of focused build work — SQL development, dashboard iteration, metric validation — with stakeholder time. You might join a working session with a Logistics or Capacity Optimization team to walk through a metric definition, then sit with one of their analysts to review a query they wrote against CIAT's datalake. After lunch you might pair with a Data Engineer on the data model behind a new WBR metric, draft the narrative for that week's review, and review another BIE's pull request before logging off. On a different day, the focus is an Amazon Q topic for a domain that has been generating repeat ad-hoc requests — designing the supporting dataset, configuring synonyms, and testing answers against real questions customers have asked.

You will be in front of customers regularly — not constantly, but enough that you should enjoy that side of the work. The team partners closely with Data Engineers and Systems Development Engineers, so you will rarely be the only person on a problem.

About the team
CIAT gathers, transforms, and analyzes data for inventory, change-of-state, system health, safety, security, workload, and resource efficiency across AWS's global data center fleet. Our customers are the operations leaders who run rack install, decommission, repair, logistics, capacity optimization, and network operations. We are investing heavily in self-service and GenAI-powered analytics to expand the reach of the team beyond what any one BIE can deliver alone.