A strategic bridge between data engineering and marketing growth.
Over 4 years architecting tag management infrastructure — GTM, Tealium, and GA4 — that evolved into marketing science through Google's Advanced Data Analytics program. I turn raw event data into predictive models, multi-touch attribution, and warehoused insight (GCP/BigQuery) that marketing spend can actually be based on.
Where clean tracking infrastructure
becomes marketing science.
Infrastructure First, Insights Second
I started in web analytics, architecting the GTM and Tealium infrastructure that other people's dashboards quietly depend on. Google's Advanced Data Analytics program pushed that further — into Python-driven predictive modeling, regression, and machine learning applied to marketing data.
Today that means connecting fragmented customer data across Salesforce and HubSpot, warehousing raw events in GCP/BigQuery, and building multi-touch attribution models that replace last-click guesswork — whether it's an enterprise application or a high-velocity media agency's campaign reporting.
Infrastructure First
Every project starts with validated, schema-clean tracking — not a hunch.
CRM-Connected
Salesforce and HubSpot integrations keep the customer record whole across teams.
Technical Methodology
The structured workflow I apply to every tracking, integration, and attribution project — from raw event to actionable model.
Audit & Tagging
- GTM & Tealium implementation audits
- Server-side tracking & Data Layer design
- Cross-platform schema validation
Integration & Warehousing
- Salesforce & HubSpot API integration
- GCP / BigQuery event warehousing
- ETL pipeline design
Modeling & Reporting
- Multi-touch attribution modeling
- Python regression & predictive models
- Looker Studio / Tableau dashboards
Ready to turn raw event data into a growth engine?
Let's talk about the tag management, CRM integration, or attribution modeling work behind your next growth initiative.