AWS

My AWS work is about extending AI telemetry beyond the Microsoft stack. I designed and built the entire pipeline myself — Python Lambda functions ingest and transform data, S3 stores it, Athena queries it, and QuickSight surfaces the results as production dashboards. The goal is to unify AI usage and operational data from sources that live outside the Microsoft ecosystem — including AWS Bedrock, third-party AI tools, and custom internal systems — into a single analytics layer. This pipeline is in active development and represents the next phase of the AI Insights Hub.

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Work examples

ZDX Device Experience Dashboard (QuickSight)

Monitors device health across 2,451 endpoints — average device score (73), 312 high-risk devices, score distribution by city, OS, device type, and risk level with 7-day trend lines. Built in AWS QuickSight on ZDX telemetry data.

Zoom Room Reliability Dashboard (QuickSight)

AWS QuickSight dashboard monitoring 99.15% uptime, 290 issue hours, MTTR, and reliability scores across 180+ Zoom Rooms globally — with room-level performance tables, uptime trend lines, and issue type breakdowns by city and region.

Screenshots shown are placeholders. Built with synthetic data — original architecture, design, and analysis are my own work.