Delivery lane
Marketing, Attribution, and Growth Data Attribution-ready pipelines for ads, analytics, mobile, and conversion reporting.Mobile attribution
AppsFlyer to BigQuery Integration
Mobile attribution you can act on, not a feed everyone double-checks before trusting.
- Python
- AppsFlyer
- dbt
- BigQuery
Problem
Mobile acquisition data is valuable, but attribution feeds often arrive fragmented and hard to trust.
Solution
Installs, in-app events, and campaign costs get normalized through a Python + dbt path built for mobile-specific quirks, not adapted from a generic ETL.
Outcome
Mobile attribution reporting becomes something growth can act on, not just a dataset to double-check.
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
- Install and event normalization
- Campaign cost reconciliation
- Analytics-ready attribution marts
- Replay-safe publish flow
Stack and operating model
- Python
- AppsFlyer
- dbt
- BigQuery
- Attribution modeling
- Ops logs
Installs, in-app events, and campaign costs normalized through a path built for mobile's specific quirks, so growth stops reconciling numbers by hand.
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.