Data Scraping & Validation Workflow

Overview

Python-based scraping workflows that collect, normalize, validate, and monitor structured data from multiple web sources, with quality checks baked into the pipeline.

Problem / Context

Structured data had to be gathered from many sources with differing formats, and downstream consumers needed it clean, consistent, and reliable. Manual collection did not scale and was error-prone.

My Role

I designed and maintained the scraping and validation workflows end to end — from extraction scripts to normalization rules, quality checks, and debugging pipeline failures.

What I Built

• Scraping scripts for structured data collection across sources
• Normalization and cleaning routines for consistent schemas
• Validation rules and automated quality checks
• Monitoring to surface failures and data drift

Tech Stack

Key Features

• Repeatable extraction and cleaning pipeline
• Validation rules that flag malformed or missing data
• Spreadsheet-based reporting for non-technical reviewers

Challenges

• Source layout changes breaking extraction logic
• Schema mismatches between sources and target format
• Detecting silent data-quality regressions early

Outcome

• More reliable, validated datasets delivered consistently
• Reduced manual effort through automation and monitoring