Analyzing Referral Programs with Modern Data Engineering

Referral programs are powerful drivers of user acquisition, but measuring their true impact requires a solid data foundation. This text-based course guides you through the process of setting up pipelines and analyzing referral data using modern data engineering workflows. You will transition from understanding raw database schemas to constructing clean, transformed data models that track user growth, attribution, and retention. By practicing with realistic case study scenarios, you will learn how to turn messy event logs into actionable business insights. What you'll learn: 1. Understand core referral program metrics, attribution models, and foundational database schemas. 2. Design and build clean data pipelines to ingest and transform referral event logs. 3. Apply SQL and modern data transformation techniques to model user growth and retention. 4. Calculate key performance indicators like viral coefficient and customer acquisition cost. 5. Identify data quality issues and implement basic testing to ensure pipeline reliability. The course starts with essential definitions and database structures before moving into hands-on SQL transformations and pipeline modeling. You will work through a structured referral case study, analyzing data step-by-step through clear written explanations and code examples. This course is designed for beginner data analysts, aspiring data engineers, and growth marketers who want to build practical data modeling skills. No prior data engineering experience is required, though a basic familiarity with SQL is helpful. Start building your data engineering toolkit and unlock deeper insights into user growth today.