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.