End-to-end analytics pipeline for sales data

The challenge

A company with sales data spread across dozens of CSV files, hand-maintained Excel sheets and a legacy CRM. Monthly reports took 2 days of manual work and were regularly inconsistent.

The approach

Architecture

A layered data architecture:

1. Source layer — Raw data from CSV, Excel and CRM exports
2. Transformation layer — Python scripts for cleaning, validation and transformation
3. Warehouse — PostgreSQL with a dimensional model (star schema)
4. Presentation layer — Power BI dashboard with a direct connection to the warehouse

Data transformation

Python scripts running daily:

• Automatic detection of new source files
• Data validation and quality checks
• Deduplication and standardization
• Loading into the warehouse with upsert logic

Dashboard

An interactive Power BI dashboard with:

• Revenue by region, product and time period
• Customer analysis: retention, lifetime value, churn indicators
• Trend analysis with year-over-year comparison
• Drill-down from national to individual salesperson

The result

• Reporting time: from 2 days to 15 minutes
• Data quality: 99.5% consistency after automated validation
• Insights: first-ever visibility into customer churn and regional trends

Technology

Python, pandas, PostgreSQL, Power BI, Power Query, DAX, cron scheduling