Chemical Engineer turned GA4 Data Integrity Auditor | Process Control, Mass Balance & First-Party SST

Most e‑commerce and SaaS companies lose 20–40% of their conversion data due to broken tracking and client‑side signal loss. But insights are only as good as the data infrastructure underneath them.

I don't approach tracking as a marketer; I approach it as a process engineer. I apply Mass Balance and Failure Mode Analysis (FMEA) to analytics infrastructure to find where the pipeline leaks and why — and now extend that into first‑party Server‑Side Tagging (SST) on GCP App Engine.

• GA4 Data Integrity Audits: 47‑point diagnostic across Shopify, GTM, and GA4 — scored matrix, dev‑ready fix tickets, and a QA monitoring dashboard
• GA4 ↔ Shopify Mass Balance Monitoring: FULL OUTER JOIN reconciliation between GA4 events and Shopify orders, with FMEA leak classification (FM‑01 tracker suppression, FM‑03 phantom purchases, FM‑05 sensor ghosts) and quantified impact
• First‑Party SST on GCP App Engine: Dual GTM Web + Server containers with custom first‑party endpoints (e.g., collect.aniji.ca) to bypass ITP/ad‑blockers and restore signal integrity, plus app.yaml + GTM JSON exports and a GitHub Pages trigger environment to validate Web → Server → GA4 delivery before production
• NL2SQL & Semantic Layer QA: Design of Experiments (DoE) stress‑testing of AI‑powered query platforms to diagnose architectural failures before they reach production
• SaaS Revenue Protection: Dual‑method SQL redundancy validation to identify at‑risk MRR before it disappears from your model

• GA4 ↔ Shopify Mass Balance Monitor & First‑Party SST Proxy — Diagnosed a 4.95% phantom ROAS over‑attribution rate, preventing an estimated ~$80,000 in phantom revenue from contaminating media models. Includes a live Looker Studio monitor and full cloud config (SQL, FMEA, migration framework, GTM exports).
• GA4 Data Integrity Audit — 47‑Point Diagnostic + Dev‑Ready Fix Plan — Engineered a PIMS‑style validation pipeline with automated SQL event testing, delivering a 47‑point diagnostic matrix, prioritized fix tickets, and a QA monitoring dashboard.
• NL2SQL Semantic Layer Audit | 16 Failure Modes Diagnosed — Executed a 30‑query DoE across 5 bug classes on an enterprise NL2SQL platform. Real client, 5‑star review.
• SaaS Churn Risk Engine | ~$250K At‑Risk Revenue — Identified quarter‑million MRR exposure using engineering redundancy validation and dual‑method SQL cross‑checks. Random Forest ML + Tableau control panel.

GA4 Google Tag Manager Shopify BigQuery SQL Python Looker Studio Scikit-learn Tableau GCP App Engine Server-Side Tagging

• Upwork (Book a GA4 Audit): https://www.upwork.com/freelancers/datacharles
• LinkedIn: http://www.linkedin.com/in/caniji
• Email: charles@aniji.ca