BI & Data Product Manager. I build governed data platforms and production AI systems for US last-mile logistics and cross-border e-commerce.
Data governance + platform architecture applied to real operational scale — where "trustworthy" beats "clever".
The platform I own serves 280+ active users across every functional department — analysts, operators, executives, and finance — with 80K+ daily API calls and a 5-minute average daily user time. Before it existed, cross-team KPI disagreements were manual, contested, and slow. Now they're a single source of truth surfaced live.
My signature is turning chaotic operational data into trustworthy, auditable, scalable systems. I've architected the full stack — from a 3-layer governed query framework, to a company-wide KPI standardization program across 14 departments, to a production AI-data agent with a 6-layer context system, 13-field PII masking, and multi-layer SQL guardrails.
I care about how governance scales, not just how it works. Every system I ship has ownership, auditability, and clear boundaries baked in from day one — because that's what makes data trustworthy at real scale, not the algorithm.
Architected and shipped a from-scratch internal data platform: Executive & Functional Dashboards, Data Asset Management, Access Control, and Feedback System. Replaced manual reconciliation across marketplace SLA, driver performance, and multi-region billing.
Designed a production-grade AI-data agent with dual-routing: a Spec-Driven deterministic engine paired with a Free Agent (GPT-4o) fallback. 13-field PII masking, multi-layer SQL guardrails, and full request-level observability across 5 business domains.
Standardized operational metrics across QA (INR, DNR, long-tail), Warehouse Ops, Customer Service, Middle Mile, Finance, and Commercial — driving cross-functional alignment as single source of truth for the Executive Dashboard.
I'm always up for a good conversation about data trust, cross-border logistics, or the messy edges of enterprise governance. If you're working on something in this space — or you know someone I should talk to — I'd love to hear.
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