Python Data Cleaning & Google Sheets Automation
The Challenge
Teams were spending 8+ hours weekly on repetitive data preparation (downloading CSVs, cleaning inconsistent formats, fixing data quality issues, running validation checks, and manually uploading to Google Sheets for stakeholder dashboards). This workflow was error-prone (15-20% manual entry error rate), delayed reporting by days, and prevented analysts from focusing on actual analysis.
What I Built
Designed and implemented an automated ETL pipeline in Python that standardizes, validates, and cleans raw datasets before pushing directly to Google Sheets via API integration.
Business Impact:
• 98% time reduction in data preparation (8 hours → 10 minutes weekly))
• 90% reduction in reporting errors (20% → 2% error rate)
• Real-time dashboard updates vs. weekly manual refreshes
Technical Approach
The pipeline was designed as a configurable framework, enabling reuse across teams and datasets without rewriting core logic.
Pipeline Architecture:
• Extract & Validate: Schema enforcement, type coercion, duplicate detection on primary keys
• Transform & Clean: Date normalization, numeric parsing, categorical standardization, boolean normalization
• Load: Google Sheets API integration with retry logic and rate-limit handling
This solution is particularly suited for early-stage SaaS teams that rely on Google Sheets for reporting but lack dedicated data engineering resources
- Overview Automated ETL pipeline eliminating 8 hours of weekly manual data prep. Reduces reporting errors by 90% while enabling real-time stakeholder dashboard updates through Python and Google Sheets API integration.
- Technologies Used Python (Pandas, NumPy), Google Sheets API (gspread), OAuth2 Authentication, Error Logging, Automated Scheduling
- Skills Demonstrated Python automation, ETL pipeline development, API integration, data validation, error handling, modular code design, workflow optimization, configuration management, production deployment
- Business Impact 98% time savings (8 hrs → 10 min weekly), 90% error reduction (20% → 2% error rate), enabled real-time updates vs. weekly manual refreshes, reusable framework saved 470+ hours annually across 3 departments