Overview
What was broken, and what had to be true.
A creator media platform specializing in influencer marketing publishes creator content and runs paid media campaigns through creator profiles on behalf of major national brands. Their business depends on granular, accurate performance data across every ad platform, creator, episode, and conversion event.
When the client came to Maven, a prior migration had stalled at roughly 30% completion. The new models did not work, the consultancy had walked away, and legacy connector pipelines could not support newer platforms or custom attribution windows.
Maven audited the failed implementation and found a data architecture crisis that required a complete rebuild. The engagement evolved into a long-term partnership to transform the client’s analytics infrastructure.
The challenge
The reporting layer could not support the decision.
Years of technical debt went beyond an incomplete migration: 100+ undocumented dbt models, 2–3 day processing delays, 40+ weekly manual hours, no native 1-day view-first attribution, coverage limited to Facebook and Google, and ~85% data accuracy visible in a client-facing Bubble app.
The implementation
One governed workflow from source to output.
Maven rebuilt the warehouse with a bronze/silver/gold/presentation architecture, Maven connectors plus custom API integrations, expanded from 2 to 7 ad platforms, and added automated discrepancy monitoring with Slack alerts—without interrupting reporting.
Implementation · Migration recovery · Custom integrations · Data quality monitoring
The approach
How the system was built.
Data architecture rebuild
Rebuilt the entire data infrastructure in BigQuery using a medallion architecture with four layers: bronze (raw + standardized naming), silver (subject-area models with cross-channel field standards), gold (fact and dimension tables), and presentation (reporting tables that power the Bubble application). Business logic moved into reusable dbt macros with documentation, automated tests, and multi-tenant configuration.
Direct API integrations
Where off-the-shelf connectors fell short, Maven combined its own connectors with custom Google Cloud integrations. Standard metrics flowed through Maven connectors; custom APIs delivered advanced attribution and conversion requirements.
- Facebook Insights API with 1-day view-first attribution and 37 months of historical pulls
- TikTok Events API for custom events and landing-page views
- Google Ads GAQL for ad-level conversion actions
- Pinterest, Snapchat, and Roku direct pipelines for targeting and connected-TV reporting
Platform expansion & monitoring
Expanded platform coverage from Facebook and Google to seven channels—Facebook, Google, TikTok, Pinterest, LinkedIn, Snapchat, and Roku—and added continuous monitoring that compares live values against validation benchmarks with Slack alerts before discrepancies reach stakeholders.
The outcome
What changed for the team.
Processing time dropped by 60%; workflows that took hours now complete in minutes
Data accuracy improved from ~85% to 99.9%
Replaced 2–3 day delays with reliable daily updates
Platform coverage expanded from 2 to 7 with creative-level reporting across 50,000+ assets
40+ weekly manual hours freed; true 1-day view-first attribution reconciled across platforms