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Automated Web Scraping & Data Extraction Pipeline
Overview: Built an automated, scalable data extraction workflow to harvest, parse, and structure nested e-commerce catalog data from dynamic web pages.
Key Deliverables:
HTML DOM inspection and robust tag traversal for complex, paginated product structures.
Resilient headless scraping pipeline using custom parsing logic to prevent data loss.
Direct ingestion into clean tabular schemas exported as analysis-ready CSV files with detailed execution logs.
Tech Stack: Python, BeautifulSoup, Selenium, Pandas, Requests.
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How are you handling IP rotation for larger scraping runs? Are you using a proxy pool with session-level rotation, or relying more on request throttling and browser-level fingerprints to reduce blocking?
A scraper that "works on my machine" but 403s in production? Check your User-Agent before anything else.
Python's default urllib User-Agent gets blocked by a huge number of sites instantly. The fix is one line — send a real browser User-Agent — but the failure looks like a hundred other things (auth, rate-limits, IP bans), so people chase the wrong bug for an hour.
The broader lesson from building scrapers daily: the code is easy, the resilience is the job — pagination, rate-limits, anti-bot headers, retries. That's where the value is.