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How to Scrape Glassdoor in 2026 (Jobs, Salaries, Company Reviews)

#webscraping #python #jobs #hr

Glassdoor holds some of the most valuable workforce data on the internet — salary ranges, company reviews, interview questions, and job listings, all contributed by real employees. For HR teams, recruiters, and market researchers, this data drives better decisions.

But Glassdoor is notoriously difficult to scrape. It requires login for most data, uses aggressive bot detection, and limits what's visible to non-authenticated users. In this guide, I'll show you how to extract Glassdoor data reliably in 2026.

What You Can Extract From Glassdoor

Glassdoor's dataset is massive and multi-dimensional:

  • Job listings — title, company, location, salary estimate, posted date, requirements
  • Salary data — base pay, total compensation, pay by experience level and location
  • Company reviews — overall rating, pros, cons, advice to management, CEO approval
  • Interview questions — difficulty rating, process description, questions asked, offer outcome

This data powers use cases across HR, recruiting, and competitive intelligence.

Why Glassdoor Is Hard to Scrape

Glassdoor is one of the more challenging scraping targets in 2026:

  1. Login required — most salary and review data is hidden behind authentication
  2. Aggressive rate limiting — even authenticated sessions get blocked after moderate request volumes
  3. Dynamic rendering — review content loads via JavaScript, requiring browser automation
  4. Anti-bot measures — fingerprinting, CAPTCHA challenges, and behavioral analysis

A naive requests-based approach will hit walls almost immediately.

The Solution: Glassdoor Scraper on Apify

I built Glassdoor Scraper to handle all of these challenges. It manages authentication, browser sessions, and proxy rotation automatically.

The actor supports four scraping modes:

1. Job Search

Extract job listings matching your criteria:

{
  "mode": "jobs",
  "query": "software engineer",
  "location": "San Francisco, CA",
  "maxItems": 100
}
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Returns job title, company name, salary estimate, location, posting date, and job description.

2. Company Reviews

Pull all reviews for a specific company:

{
  "mode": "reviews",
  "companyUrl": "https://www.glassdoor.com/Reviews/Google-Reviews-E9079.htm",
  "maxItems": 200
}
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3. Salary Data

Extract salary information by role and company:

{
  "mode": "salaries",
  "companyUrl": "https://www.glassdoor.com/Salary/Google-Salaries-E9079.htm",
  "maxItems": 50
}
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4. Interview Questions

Get interview experience reports:

{
  "mode": "interviews",
  "companyUrl": "https://www.glassdoor.com/Interview/Google-Interview-Questions-E9079.htm",
  "maxItems": 50
}
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Sample Output

Here's what company review data looks like:

{
  "company": "Google",
  "overallRating": 4.3,
  "reviewTitle": "Great engineering culture but growing pains",
  "pros": "World-class engineering team, excellent benefits, meaningful projects at scale",
  "cons": "Bureaucracy increasing, promotion process can feel political",
  "rating": 4,
  "employmentStatus": "Current Employee",
  "jobTitle": "Senior Software Engineer",
  "reviewDate": "2026-03-10",
  "recommendToFriend": true,
  "ceoApproval": true
}
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And salary data:

{
  "jobTitle": "Software Engineer",
  "company": "Google",
  "basePay": {
    "low": 135000,
    "median": 162000,
    "high": 195000
  },
  "totalCompensation": {
    "low": 180000,
    "median": 280000,
    "high": 400000
  },
  "currency": "USD",
  "sampleSize": 2847
}
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Use Cases

HR and compensation teams: Benchmark your salary bands against market data. Scrape salary info for your key roles across competitor companies to ensure competitive offers.

Recruitment agencies: Build comprehensive employer profiles for candidates. Combine review sentiment, salary data, and interview difficulty to give job seekers the full picture.

Employer branding: Monitor your own Glassdoor reviews in real-time. Track sentiment trends and catch negative patterns early before they affect recruiting.

Market research: Compare employee satisfaction across an entire industry vertical. Identify which companies are gaining or losing talent based on review trajectories.

Proxy Strategy

Glassdoor's bot detection is sophisticated, so proxy quality matters. The Apify actor uses residential proxies by default for maximum reliability. If you're building a custom scraper, you'll need premium residential proxies — services like ScrapeOps let you compare proxy providers and route through the best-performing ones for Glassdoor specifically.

Scheduling and Data Pipeline

For ongoing monitoring, schedule the Glassdoor Scraper to run weekly on Apify. Export options include:

  • JSON/CSV — download directly or push to cloud storage
  • Google Sheets — for team-accessible dashboards
  • Webhooks — trigger downstream processing when new data arrives
  • API — pull results programmatically into your own applications

A typical setup: weekly review scrapes for your company and top 5 competitors, piped into a dashboard that tracks rating trends.

Legal Considerations

Glassdoor's Terms of Service restrict automated access. Use scraped data for internal research and analysis. Don't republish reviews or salary data publicly, and respect the platform's rate limits.

Wrapping Up

Glassdoor data is incredibly valuable for workforce intelligence, but the platform's anti-bot defenses make extraction challenging. A purpose-built scraper that handles authentication and proxy rotation takes the pain out of the process.

Try the Glassdoor Scraper on Apify to see what insights you can extract from the workforce data that matters to your team.

Part of the Scraping in 2026 series. Follow for more guides on extracting data from popular platforms.

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