Built serverless ELT pipeline on GCP with event-driven Cloud Functions, BigQuery transformations, and Looker Studio dashboard. Automatically processes SaaS CSV uploads (<30s latency) to visualize MAU, churn, and growth metrics. Fully automated from ingestion to BI.
SaaS companies need to analyze product usage data to understand user behavior, track growth, and reduce churn. Manually processing raw data files is slow, error-prone, and doesn't scale, creating a delay between data availability and actionable insights.
Developed a fully automated pipeline where raw CSVs uploaded to a GCS bucket trigger a Python Cloud Function. This function ingests the data into a raw BigQuery schema. A series of SQL scripts then transform this data into clean, aggregated analytical tables, which are connected to an interactive Looker Studio dashboard for business intelligence.
Created a data pipeline for processing raw data files.
Enabled near real-time analysis of key SaaS business metrics like user growth and churn.
Built a scalable and cost-effective solution using serverless GCP components.