Francis C.

Web Scraping & Data Extraction specialist — 400+ Upwork projects, 5,000+ websites scraped, Top Rated Plus. I build reliable, production-ready scrapers and ETL pipelines in Python that turn websites, mobile apps, APIs, databases, and raw files into clean, structured, decision-ready data.. Whether the source is a JS-heavy site, an authenticated portal behind login, a mobile app, a public or hidden API, a database, or a raw dataset, I design the right solution — then clean, validate, deduplicate, and deliver it as CSV, Excel, JSON, database, or API. 🔍 Tools Python · Scrapy · Selenium · Playwright · BeautifulSoup · Frida · Jadx · mitmproxy · pandas · SQL · AWS ✨ Core Expertise ▸ Web Scraping & Crawling — large-scale crawler pipelines with session handling, pagination, retries, and validation. Static sites, dynamic JS-rendered pages, e-commerce, real estate, store locators, and sites behind login. ▸ Mobile App Scraping & Reverse Engineering — extracting data from Android apps, including API discovery and encryption/decryption via Frida, Jadx, and mitmproxy. A genuine specialty most scrapers can't deliver. ▸ Data Extraction & Mining — business, product, pricing, contact, reviews, listings, real estate, insurance, and lead-gen datasets from websites, apps, APIs, databases, PDFs, and raw files. ▸ Python Automation & API Work — hidden-API discovery, authenticated API clients, scheduled automation, price monitoring, and change tracking (sessions, cookies, headers, tokens). ▸ ETL & Data Engineering — cleaning, transformation, validation, enrichment, deduplication, and delivery with Python, pandas, SQL, and AWS at scale. 🎯 Selected Projects ▸ Scraped 100M+ employee reviews across 20 countries for a Harvard Business School study — a scalable Python pipeline handling pagination, rate limiting, deduplication, and multi-region coverage. ▸ Parsed 2TB of machine-readable (MRF) files on AWS to extract structured insurance pricing — streaming/iterative parsing far larger than memory, normalized into clean tables. ▸ Tracked 816 store locators across US and EU retail chains with scheduled crawler + ETL pipelines to detect new openings — a signal used to predict real-estate prices. ▸ Built multi-source scrapers extracting 500K+ business records from Yelp, Google Maps, and similar — name, address, phone, email, website, category — deduped and delivered in CSV, Excel, JSON. ✅ Why Clients Hire Me ▸ Production-ready scrapers and ETL pipelines built to your exact data needs ▸ Hard-to-reach sources handled — anti-bot protection, login walls, mobile apps, hidden APIs ▸ Clean, deduplicated data in your preferred format ▸ Clear communication, reliable delivery, documentation, and ongoing maintenance 🤝 Next Step Share the source, the fields you need, and your delivery format — I'll come back with a clear plan, timeline, and exactly what I can deliver. Browse my portfolio below for examples and reviews.