Key Learnings

Technical Growth

1. ETL Design Principles

• Learning: Raw data preservation is crucial for debugging and reprocessing

• Application: Always maintain audit trails and version control for data transformations

2. Scalable Architecture Decisions

• Learning: Over-engineering early wastes time, but planning for scale prevents rewrites

• Application: Started simple with Firebase, designed for BigQuery scalability

Business Impact Understanding

3. Data Infrastructure as Strategic Asset

• Learning: Proper data collection becomes more valuable over time

• Application: Invested 20% extra effort in data quality to unlock long-term insights

4. Cultural Change Through Accessibility

• Learning: Beautiful dashboards don't create data culture - easy access to actionable insights does

• Application: Prioritized stakeholder training and self-service capabilities over advanced features

Cross-Functional Collaboration

5. Understanding Diverse Analytics Needs

• Learning: Each team needs different data granularity and update frequencies

• Application: Designed flexible data models supporting multiple use cases

Tools & Technologies

• Data Collection: Google Firebase Analytics & Events

• Data Storage: Google BigQuery (data warehouse)

• Data Processing: Python (pandas, numpy), SQL

• Visualization: Google Data Studio, Custom Python dashboards

• Infrastructure: Google Cloud Platform