I'm the analyst who also builds the thing. I model data with SQL and dbt, and I ship the dashboards and apps around it, so teams get a working system instead of a slide. My sharpest lane is RevOps / GTM analytics — the Salesforce + SQL + dashboarding combination most analysts don't have. MS in Business Analytics from Arizona State (W. P. Carey, GPA 3.91). I work a 2 PM–11 PM IST block, giving 4+ hours of daily overlap with US-East mornings and full overlap with Europe.
Built the daily pacing and conversion review the sourcing and revenue team runs on: 18 charts across 9 pipeline stages over ~190K accounts, on an hourly systemd refresh with Slack failure alerts, so leadership always had current numbers before standup regardless of timezone.
Ran an 89,154-record backfill across 5 Account fields via the Bulk API in 200-record batches with zero data loss, and provisioned a new field through the Metadata API. Followed with a data-hygiene audit across 8 objects and 7 enrichment vendors, quantifying 17+ issues into a prioritized cleanup roadmap.
For a 3PL logistics client, built a SQL reconciliation across billing and invoicing data and surfaced roughly $280K in annualized revenue leakage. Part of an independent-consulting run delivering dbt/SQL pipelines and 12+ dashboards across DTC, logistics, and B2B-SaaS clients.
A bilingual progressive web app with a map, leaderboard, offline queue, and an admin dashboard. Full product, designed and built solo and deployed live.
Multi-role web ERPs (PIN logins, audit logs, ledgers, payments, Excel import/restore) running in production for real businesses. Data model to UI, shipped and maintained.
Gradient Boosting model, 86% accuracy over 170K+ shipment records, deployed to a Tableau monitoring layer used by an operations control center.
Credit-risk modeling, CSAT prediction, Streamlit apps, and analytics utilities. The repos behind the case studies above.