Delivery lane
Marketing, Attribution, and Growth Data Attribution-ready pipelines for ads, analytics, mobile, and conversion reporting.Web analytics integration
GA4 to BigQuery Integration
A GA4 export your team can build channel reporting on, instead of a raw dump nobody quite trusts.
- Python
- GA4
- dbt
- BigQuery
Problem
GA4 exports are useful but messy when teams need stable warehouse models and channel reporting.
Solution
Sessions, conversions, and traffic-source data get normalized into stable models, with ops logs auditable enough for a finance or growth review.
Outcome
GA4 becomes a source teams can build channel reporting on, instead of a raw export everyone quietly distrusts.
Repository scope
4 core delivery blocks A compact build with practical deliverables and visible operating behavior.Operating posture
Attribution-ready marketing data Built with the same logging, retries, and validation you would expect from a workload running in production.What ships in this repository
- Normalized sessions and conversions
- Traffic-source reporting layer
- Warehouse-ready analytics marts
- Replay and dead-letter controls
Stack and operating model
- Python
- GA4
- dbt
- BigQuery
- Traffic source marts
- Ops logs
Sessions, conversions, and traffic-source data normalized into stable models, with logs clean enough to survive a finance or growth review.
Why buyers pick this type of build
- Connects media, analytics, and CRM flows without duplicate syncs or broken attribution.
- Works well for growth teams that need reporting and activation from the same delivery path.
- Keeps hourly or daily operations visible with retry, dead-letter, and replay-safe behavior.
How delivery stays reliable
- Structured logs, validation steps, and predictable run behavior.
- Replay-safe processing paths designed to reduce duplicate work and broken publishes.
- Repository structure that another engineer can extend without re-learning the whole system.