Financial Leads Web Scraping Automation

I need a robust scraping solution that continuously gathers fresh small-business leads for my financial services outreach. The goal is to capture ownership details, company names, phone numbers and any other publicly available contact information, then deliver the data in CSV, JSON. Because the breadth of sources can vary from business directories and company websites to social platforms—and potentially any other page that reveals the information—I’m looking for a developer comfortable mixing and matching techniques: headless browsers for dynamic pages, straight HTTP requests where possible, and smart rate-limiting or proxy rotation to keep everything compliant and undetected. You’ll likely rely on Python (Scrapy, BeautifulSoup, Selenium or Playwright) as well as TypeScript where it makes sense, for example in a Node-based microservice or a lightweight dashboard that lets me trigger new scraping jobs and download the resulting datasets. I don’t mind which framework does what as long as the whole pipeline is clear, well-documented and easy for me to redeploy. Deliverables: • Scraper code with clear setup instructions • Automated pipeline that outputs the same dataset in CSV, JSON and SQLite after each run • Read-me outlining how to add or swap sources and how to respect [login to view URL] / rate limits • One sample run that proves you can pull owner name, company name and phone number from at least three distinct source types (e.g. a directory, a social page and an official website) If you’ve tackled similarly broad lead-generation scrapes before, especially in the financial or B2B space, I’d love to see a brief example or repo link.

Project ID: 40487099