Extract Job Listings from Indeed — Python Tutorial 2026
Indeed is the world's largest job search engine with 250M+ monthly visitors. Here's how to extract job listing data programmatically.
Why Scrape Indeed?
- Market salary analysis
- Job demand trends
- Recruitment automation
- Competitive intelligence
Quick Start with Apify
Use the Indeed Job Scraper on Apify — no coding needed. Just enter keywords and location, get structured JSON back.
Python Method
import httpx
from bs4 import BeautifulSoup
def search_indeed(keyword, location):
url = f"https://www.indeed.com/jobs?q={keyword}&l={location}"
resp = httpx.get(url, headers={ "User-Agent": "Mozilla/5.0" })
soup = BeautifulSoup(resp.text, "lxml")
jobs = []
for card in soup.select(".job_seen_beacon"):
title = card.select_one(".jobTitle")
company = card.select_one(".companyName")
if title and company:
jobs.append({
"title": title.get_text(strip=True),
"company": company.get_text(strip=True),
})
return jobs
What Data You Can Extract
- Job title and description
- Company name and rating
- Salary range
- Location
- Posting date
- Application link
Best Practices
- Use polite delays between requests
- Rotate user agents
- Consider using a proxy service
- Check Indeed's terms of service
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Top comments (1)
I appreciate your insights on scraping job listings, particularly the emphasis on best practices like using polite delays and rotating user agents to avoid IP bans. Implementing these techniques not only enhances the scraper's reliability but also aligns with ethical scraping practices. It might be beneficial to integrate a simple logging mechanism to track the success of requests and any errors encountered during the scraping process. If you’re looking for additional engineering support to refine or expand this implementation, I’d be glad to explore a paid collaboration. What are your thoughts on adding more data points for analysis, such as job benefits or required skills?