Workflow Automation Marketing Analytics
Automated ETL Pipeline Processing 2M+ Records Daily
The challenge
A marketing analytics firm was pulling data from 15+ advertising platforms (Google Ads, Meta, TikTok, LinkedIn, etc.) for 200+ clients. Their data team spent the first 3 hours of every day manually downloading CSVs, reformatting them in Excel, and uploading to their reporting tool. Data was always 24 hours stale, inconsistencies between platforms caused reporting errors, and onboarding a new client took 2 weeks of setup.
What we built
We built a fully automated ETL pipeline that ingests, transforms, and unifies data from all advertising platforms into a single source of truth.
- 01
Data Extraction with Python + Airflow
Built Python extractors for 15 advertising platform APIs, orchestrated by Apache Airflow DAGs. Each extractor handles authentication, pagination, rate limiting, and error recovery. Jobs run every 6 hours with automatic retries on failure.
- 02
Transformation & Normalization Layer
Python transformation scripts normalize disparate data formats into a unified schema — standardizing campaign names, currency conversions, attribution models, and metric definitions across all platforms. Data quality checks catch anomalies before they reach the dashboard.
- 03
Loading to Supabase Data Warehouse
Transformed data loads into Supabase with partitioned tables for fast queries. Materialized views pre-compute common aggregations — spend by channel, ROAS by campaign, and trend data. Client dashboards query these views for instant results.
- 04
Monitoring & Alerting via N8N
N8N workflows monitor pipeline health — job failures, data freshness, and volume anomalies trigger instant Slack alerts. A daily digest shows records processed, pipeline latency, and any data quality issues requiring attention.
Built with
Delivered in 5 weeks
Python
Extractors, transformations & scripts
Airflow
DAG orchestration & scheduling
Supabase
Data warehouse & materialized views
N8N
Monitoring, alerts & notifications
Slack
Pipeline health alerts
Docker
Containerized deployment
The outcome
The pipeline processes over 2 million records daily across 200+ client accounts. Manual data work was eliminated by 98%. Data freshness improved from 24 hours to 6 hours. New client onboarding dropped from 2 weeks to 15 minutes — just connect the API keys and the pipeline handles the rest.
- Records Daily
- 2M+
- Manual Data Work
- -98%
- Data Freshness
- 6hrs
- Client Onboarding
- 15min
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