Workflow Automation/Marketing Analytics

Automated ETL Pipeline Processing 2M+ Records Daily

2M+

Records Daily

-98%

Manual Data Work

6hrs

Data Freshness

15min

Client Onboarding

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.

Our Solution

We built a fully automated ETL pipeline that ingests, transforms, and unifies data from all advertising platforms into a single source of truth.

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.

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.

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.

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.

Tech Stack

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.

2M+