Delivery lane
Marketing, Attribution, and Growth Data Attribution-ready pipelines for ads, analytics, mobile, and conversion reporting.Paid media integration
Meta Ads Data Pipeline
Paid media numbers that hold up hour over hour, without someone patching a spreadsheet.
- Python
- Meta Ads
- dbt
- BigQuery
Problem
Paid media teams need clean marketing data fast, but hourly syncs often duplicate, drift, or break silently.
Solution
Idempotent syncs and UTM normalization stop duplicate rows before they happen, with validation queries flagging anything that still looks off.
Outcome
Paid media reporting holds up hour over hour, and nobody has to open a spreadsheet to patch it.
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
- Raw-to-analytics paid media modeling
- UTM normalization and attribution support
- Retry-safe publish and dead-letter flow
- Hourly operational observability
Stack and operating model
- Python
- Meta Ads
- dbt
- BigQuery
- Outbox
- GitHub Actions
Campaign, spend, click, lead, and conversion data synced idempotently, so a duplicate row doesn't quietly inflate the numbers marketing reports on.
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.