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Python Data Processing Automation
Custom Python automation fetching data from multiple sources, validating quality, transforming formats, generating reports emailed to stakeholders.
Reduced 6 hours to 15 minutes daily
Client
Client
Tech Stack
Python, Pandas, PostgreSQL, APIs
YEAR
Nov 2025
The Problem:
Manual data process took 6 hours daily
Data came from multiple sources in different formats
Manual validation meant errors slipped through
1,560 hours/year wasted = $78,000 annual cost
Team frustrated doing repetitive work instead of analysis
The Challenge:
Multiple formats: Each API returned data differently → Built custom parsers per source
Quality assurance: 10,000 records too many to spot-check → Implemented 15 automated validation rules
API failures: System failed if APIs went down → Built retry logic & fallbacks
Performance: Loading 10,000 records daily slowed system → Added strategic indexes
Debugging: Hard to find issues in production → Comprehensive logging at every step
The Solution:
Python script that automates entire pipeline
Runs automatically every morning at 6 AM
Fetches from 3+ sources, validates, transforms, generates reports
Zero human intervention needed
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