Automated Daily Email Scraper

I need a reliable developer to build a fully-automated system that gathers fresh, publicly listed email addresses from Google search results (pulled through a SERP API or an equivalent method) every single day, verifies them for validity, and delivers a clean CSV ready for my marketing campaigns.

Here’s what I’m after:

• Workflow
1. Submit a set of keywords or niche phrases.
2. Crawl the top Google result pages returned by a SERP service, extract any visible email addresses, and capture the source URL and page title.
3. Run each address through an SMTP-level verifier (ZeroBounce, NeverBounce, or an in-house Python verifier—whichever you prefer, as long as it returns status codes for valid, invalid, catch-all, disposable, and role accounts).
4. Output only “valid” or “catch-all” emails in a downloadable CSV along with their metadata.

• Technical notes
- I’m comfortable with Python (Scrapy, Requests, BeautifulSoup, Selenium) or Node (Puppeteer, Cheerio); choose whichever stack you can scale and maintain.
- Respect Google’s ToS with rotating residential proxies or a paid SERP API to avoid blocking and captchas.
- The job should run via a daily cron or cloud function and log results to a lightweight dashboard (even a simple Flask/Express UI or Google Sheet is fine).
- Code must be modular so I can swap in new keywords, adjust verification thresholds, or change the SERP provider without rewriting everything.

Acceptance criteria
• Daily schedule triggers without manual intervention.
• Minimum 95 % deliverability rate on the “valid” list according to the verifier report.
• Duplicate and role-based addresses removed automatically.
• Well-documented README plus a short Loom video walking through setup and adding new keywords.

If you’ve built similar data pipelines or have clever ideas on keeping the scrape respectful yet thorough, I’m all ears.