01
Scheduled ingestion
Airflow jobs collect Meta, GA4, app, and creative inputs into controlled staging paths.
Creative/Campaign Intelligence Data Platform
FeaturedCase Study
Marketing analytics platform backbone for Meta/GA4 ingestion, BigQuery transformations, AI-assisted creative analysis, signal outputs, and app-facing syncs.
Marketing Analytics ProductClient engagement - Selected workProduct team
System architecture
Architecture / Flow
The practical path from source data to reliable reporting output.
02
Warehouse modeling
BigQuery staging and merge patterns organize campaign, creative, analytics, and app-facing data.
03
AI analysis
Gemini-assisted creative analysis runs against prepared campaign context with traceable prompts and outputs.
04
Signal sync
Validated signals and analysis outputs sync back to application tables for product workflows.
Project Overview
Built and supported a creative and campaign intelligence data platform that ingests advertising and analytics data, stages and merges it in BigQuery, runs AI-assisted creative analysis, stores validation outputs, and syncs processed signals back to an application database. The system supports creative analysis, campaign signals, data quality checks, and product-ready reporting outputs.
Key Challenges
- Advertising, analytics, app, and AI-analysis data needed to move through one reliable platform with controlled access and clear data boundaries
- Multiple ingestion workflows needed repeatable orchestration, worker isolation, and predictable BigQuery load behavior
- Creative analysis outputs needed traceable prompts, structured outputs, and safe downstream sync patterns
- Data quality checks had to make freshness, row counts, duplicates, and transformation issues visible before product workflows depended on them
Results & Impact
- Built config-driven Airflow pipeline patterns for multiple marketing and analytics workflows
- Implemented BigQuery staging-to-merge load patterns with synced timestamps and row-hash style change tracking
- Supported AI creative analysis and clustering outputs for downstream product workflows
- Added validation and app-facing sync patterns so processed signals could be monitored after delivery
Technology Stack
Project Details
Industry:Marketing Data Platforms
Duration:Client engagement
Team Size:Product team
Completed:Selected work
Tags
airflowbigquerymeta-apiga4supabaseai-analysiscreative-intelligencegeminidata-qualitymarketing-data-platform
Have a similar data workflow?
If your reporting process depends on APIs, spreadsheets, ad platforms, or asynchronous exports, I can help turn it into a reliable pipeline with validation, monitoring, and clean outputs.