Scalable Analytics Architecture for B2B SaaS

I launched enterprise-grade analytics infrastructure integrating GA4, GTM, CRM, and BigQuery into a unified data pipeline, bridging the gap between marketing and engineering while maintaining GDPR/CCPA compliance. For the first time, teams could trace a visitor's journey from first click to closed deal.

Company
Cross-Company
Timeline
2023 to 2026
Role
Sr. Manager, Website Marketing
Stack
GA4 · GTM (client-side + server-side) · CookieScript · BigQuery
5 Layers
Integrated analytics architecture
Full-Funnel
First-touch to closed-deal attribution
Compliance-First
GDPR/CCPA consent management built in

The Problem

At both Pushpay and Linnworks, marketing analytics existed in silos. GA setups were inconsistent, event taxonomies were ad hoc, there was no connection between web behavior and CRM pipeline data, and no framework for privacy compliance. Marketing couldn't easily prove which activities drove revenue. Other teams couldn't trust the data marketing was producing.

The Hypothesis

If we designed the analytics infrastructure like a product, modular, documented, compliance-aware, and built for both marketing and sales to use, we could eliminate the attribution gap and enable data-informed decisions at every level.

The method

I developed a comprehensive technical strategy covering five interconnected layers: GA4 event taxonomy and configuration, GTM container architecture with server-side tagging, CRM data integration and lifecycle mapping, BigQuery data warehouse design, and compliance-first data governance including consent management, data retention, and right-to-deletion workflows.

The architecture was modular, each layer implementable incrementally while maintaining data integrity. I also conducted GA4 and GTM audits at both companies, systematically identifying misconfigured events, tag firing issues, and data loss points.

The Outcome

For the first time, marketing could trace a visitor's journey from first organic click to closed deal. Dashboards connected web metrics to pipeline outcomes, enabling quarterly business reviews grounded in data. The architecture scaled across multiple brands; Pushpay, Resi, and Linnworks. Sales teams trusted the data because the taxonomy was documented and the governance was rigorous.

Reflection

This is the work that separates a strategic marketing leader from a campaign executor. Most marketers consume analytics; I architect them. The ability to design enterprise-grade data infrastructure while maintaining privacy compliance is rare, and it's the foundation everything else I do rests on. You can't optimize what you can't measure, and you can't measure what you haven't architected.


Skills Demonstrated
Analytics
Data Architecture
GA4
Systems Thinking
Attribution Modeling
BigQuery
Consent Management
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Senior IC and consulting roles in web strategy, growth, and analytics.

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