Python Automation & Web Scraping
Custom Python scripts and scrapers that collect, transform, and deliver data on schedule.
When off-the-shelf tools reach their limits, Python fills the gap. I build async scrapers, data collectors, transformation scripts, and scheduled jobs — properly tested, containerised, and observable.
Use Cases
Competitor and market monitoring
Scrape product pages, pricing, reviews, or job postings on a schedule. Results go to a Postgres table, a Notion database, or a Slack digest.
Lead data enrichment
Given a list of company names or domains, enrich with LinkedIn data, tech stack signals, news mentions, and funding status.
E-commerce price tracking
Monitor competitor prices across multiple platforms. Alert when prices change beyond a threshold. Track over time for trend analysis.
Internal reporting automation
Pull data from multiple SaaS APIs (Google Analytics, Stripe, HubSpot), aggregate, and format into a weekly report delivered to Slack or email.
Common Questions
Is web scraping legal?
Depends on the site. I review terms of service and robots.txt for every project. Publicly available data is generally fair game; authenticated content or ToS-prohibited scraping I decline.
What if the website has bot protection?
Playwright with stealth plugins handles most bot detection. For aggressively protected sites, I use residential proxy rotation and human-like interaction patterns.
How do you handle site changes breaking the scraper?
I build monitoring that alerts when expected selectors or data shapes disappear. Most scrapers can be patched in under an hour when a site updates.
What you get
- →Async HTTP scraping with httpx / aiohttp
- →Browser automation with Playwright (for JS-rendered content)
- →Bot detection evasion (stealth, proxy rotation, fingerprinting)
- →Data parsing and transformation with Pydantic
- →Job queue management (Postgres-backed or Redis)
- →Containerised deployment with Docker
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