Project_Customer_Churn_Bank

Overview: An end-to-end ELT pipeline built to analyze ABC Bank's customer retention by integrating internal demographics with external 2022 Eurostat market benchmarks. The project focuses on identifying "Premium Segment" churn, discovering that 80% of churned customers are high-earners. How I used Bruin: I leveraged Bruin to move away from traditional script-based workflows to a modern, declarative asset-based architecture: Infrastructure as Code (IaC): Every BigQuery table and view was defined as a Bruin asset, including physical layer optimizations like Clustering on country and gender to boost query performance. Data Lineage & Dependencies: Using Bruin's DAG capabilities, I ensured a clean Medallion-like flow: Staging (cleaning) :arrow_right: Reference (Eurostat data) :arrow_right: Fact (Salary benchmarking logic). Automated Data Quality: I integrated built-in quality checks (not_null, unique) directly into the asset definitions, ensuring that only validated data reached my Looker Studio dashboard. Seamless Deployment: Bruin managed the entire lifecycle from Kaggle ingestion through GCS to BigQuery materialization with a single --force execution command. Key Findings: The pipeline revealed that churned customers earn an average of 5,048 EUR MORE than the national benchmark, and identified a 70.45% churn rate among the 46-60 age group in Germany, providing the bank with clear targets for retention programs.