Systems I designed and built in production at enterprise and global scale. The work spans 25+ years of in-house roles at AWS, Bloomberg, IBM, the New York Daily News, the NYC Board of Education, and ICP — and, through those roles, delivery for Fortune 1000 customers who can't be named. Each system is called by a descriptive name here. What follows is the capability, the problem it solved, and the service it maps to.
An ownership-and-reporting model that turns unmanaged cloud spend into a governed operating number: cost allocation and tagging discipline, unit-economics views by environment and workload, and a decision cadence that puts engineering and finance on the same number. Built for organizations where spend had outgrown the visibility around it.
A validation and scoring layer that grades data quality and completeness before anything downstream consumes it — ending the “whose number is right?” debate that stalls cost and performance decisions. Became the trust foundation for reporting, forecasting, and later AI workloads.
An AI-native executive intelligence system: governed data in, action-oriented executive pages out — with AI-summarized narrative, recommendations, and an automated operating-review pipeline feeding forecasting and planning. Adopted organization-wide as the standard for executive reporting.
Customer-health scoring and churn-risk modeling built where none existed — giving leadership a data-driven view of renewal risk and changing how account teams prioritized their time.
Replaced a slow, manual operating-review process with a serverless, AI-driven pipeline: it gathers program signals from work-management and knowledge systems, composes a structured executive narrative, then generates and distributes the finished report automatically. Built as infrastructure-as-code with least-privilege security and full observability — a repeatable pattern for any recurring reporting grind.
Replaced manual, hand-built server provisioning with automated, policy-driven deployment across a global estate — scaling from a modest footprint to tens of thousands of hosts without adding proportional headcount.
A revenue-operations framework with P&L dashboards and the analytics infrastructure beneath them — giving field leadership real-time visibility into performance, churn risk, and renewal health across multiple industry segments for the first time.
The business-operations function for a large global services business, built from a small founding team into a multidisciplinary global capability: capacity planning, KPI scorecards, the annual operating-plan cycle, and the weekly, monthly, and quarterly executive review cadence leadership ran the business on.
A hub-and-spoke segmentation and customer-tiering model for a fast-growing support organization that had outgrown its operating model — plus the multidisciplinary team assembled to run it.
A centralized analytics platform built from a blank whiteboard for a global field organization that had data everywhere and insights nowhere — plus a bi-directional data framework and an insights catalog. The AI-ready data layer beneath it became the foundation for predictive modeling and generative-AI integrations.
A unified platform that ingested and reconciled hundreds of disparate operational systems — configuration, ticketing, monitoring — into one trustworthy view of an entire infrastructure estate and its lifecycle. The engineering organization ran on it.
Migration of a mission-critical, globally used product from legacy proprietary systems to commodity Linux at enterprise scale — and the organization's first private-cloud platform built alongside it — executed with zero production disruption on a product that cannot go down.