How to Scrape Glassdoor Data in 2026

Why scrape Glassdoor?

Glassdoor data powers real business decisions across hiring, competitive intelligence, and market research:

Salary research

Benchmark compensation across roles, locations, and companies. Build salary databases for HR tools.

Company reviews

Analyze employer sentiment at scale. Track culture scores, management ratings, and work-life balance trends.

Interview prep

Collect interview questions by company and role. Build prep tools or analyze hiring difficulty trends.

Method 1: DIY scraping

Glassdoor renders most content server-side, so you can start with requests + BeautifulSoup in Python or Puppeteer in Node.js. Here are working examples:

scraper.py

#E8A0BF">import requests
#E8A0BF">from bs4 #E8A0BF">import BeautifulSoup

headers = {
    #A8D4A0">"User-Agent": #A8D4A0">"Mozilla/5.0 (Windows NT 10.0; Win64; x64) "
                   #A8D4A0">"AppleWebKit/537.36 Chrome/124.0.0.0 Safari/537.36"
}

# Glassdoor company reviews page
url = #A8D4A0">"https://www.glassdoor.com/Reviews/Google-Reviews-E9079.htm"
resp = requests.#87CEEB">get(url, headers=headers)
soup = BeautifulSoup(resp.#87CEEB">content, #A8D4A0">"html.parser")

# Extract overall rating
rating = soup.#87CEEB">select_one(#A8D4A0">'[data-test=#A8D4A0">"rating-info"] .mr-xsm')
#E8A0BF">print(f#A8D4A0">"Rating: {rating.#87CEEB">text #E8A0BF">if rating #E8A0BF">else #A8D4A0">'N/A'}")

# Extract individual reviews
reviews = soup.#87CEEB">select(#A8D4A0">'[data-test=#A8D4A0">"review-list"] li')
#E8A0BF">for review in reviews[:5]:
    title = review.#87CEEB">select_one(#A8D4A0">'.review#FFB347">-details__title')
    pros = review.#87CEEB">select_one(#A8D4A0">'[data-test=#A8D4A0">"pros"]')
    cons = review.#87CEEB">select_one(#A8D4A0">'[data-test=#A8D4A0">"cons"]')
    #E8A0BF">print(f#A8D4A0">"Title: {title.#87CEEB">text.strip() #E8A0BF">if title #E8A0BF">else #A8D4A0">'N/A'}")
    #E8A0BF">print(f#A8D4A0">"Pros: {pros.#87CEEB">text.strip() #E8A0BF">if pros #E8A0BF">else #A8D4A0">'N/A'}")
    #E8A0BF">print(f#A8D4A0">"Cons: {cons.#87CEEB">text.strip() #E8A0BF">if cons #E8A0BF">else #A8D4A0">'N/A'}")
    #E8A0BF">print(#A8D4A0">"---")

Pain points

!Glassdoor sits behind Cloudflare — simple HTTP requests often return challenge pages
!Many salary and interview pages require login, complicating automated access
!Rate limiting is aggressive — you will get 403s within dozens of requests
!Review pagination uses dynamic loading that breaks static scrapers
!Selectors change frequently as Glassdoor updates their frontend
!IP bans are sticky — once blocked, the same IP stays blocked for hours

Method 2: SnapRender API

SnapRender handles Cloudflare bypass, browser rendering, and data extraction in a single API call. Use /render for markdown output or /extract for structured JSON.

Render as markdown

Get the full review page as LLM-ready markdown — ideal for sentiment analysis pipelines.

render.py

#E8A0BF">import requests

# Render Glassdoor reviews #E8A0BF">as markdown
render = requests.#87CEEB">post(
    #A8D4A0">"https://api.snaprender.dev/v1/render",
    headers={#A8D4A0">"x-api-key": #A8D4A0">"sr_live_YOUR_KEY"},
    json={
        #A8D4A0">"url": #A8D4A0">"https://www.glassdoor.com/Reviews/Google-Reviews-E9079.htm",
        #A8D4A0">"format": #A8D4A0">"markdown",
        #A8D4A0">"use_flaresolverr": #E8A0BF">True
    }
)
#E8A0BF">print(render.#87CEEB">json()[#A8D4A0">"data"][#A8D4A0">"markdown"])

Extract structured data

Pull specific fields with CSS selectors. Returns clean JSON — no HTML parsing required.

extract.py

#E8A0BF">import requests

# Extract structured review data #E8A0BF">with CSS selectors
extract = requests.#87CEEB">post(
    #A8D4A0">"https://api.snaprender.dev/v1/extract",
    headers={#A8D4A0">"x-api-key": #A8D4A0">"sr_live_YOUR_KEY"},
    json={
        #A8D4A0">"url": #A8D4A0">"https://www.glassdoor.com/Reviews/Google-Reviews-E9079.htm",
        #A8D4A0">"use_flaresolverr": #E8A0BF">True,
        #A8D4A0">"selectors": {
            #A8D4A0">"company": #A8D4A0">"h1[data-test=#A8D4A0">'employer-name']",
            #A8D4A0">"rating": #A8D4A0">"[data-test=#A8D4A0">'rating-info'] .mr-xsm",
            #A8D4A0">"review_count": #A8D4A0">"[data-test=#A8D4A0">'review-count']",
            #A8D4A0">"recommend": #A8D4A0">"[data-test=#A8D4A0">'recommend-pct']"
        }
    }
)
#E8A0BF">print(extract.#87CEEB">json())

Example response

response.json

{
  #A8D4A0">"status": #A8D4A0">"success",
  #A8D4A0">"data": {
    #A8D4A0">"company": #A8D4A0">"Google",
    #A8D4A0">"rating": #A8D4A0">"4.4",
    #A8D4A0">"review_count": #A8D4A0">"32,847 reviews",
    #A8D4A0">"recommend": #A8D4A0">"86% would recommend to a friend"
  },
  #A8D4A0">"url": #A8D4A0">"https://www.glassdoor.com/Reviews/Google-Reviews-E9079.htm",
  #A8D4A0">"elapsed_ms": 3420
}

Legal considerations

Web scraping law is nuanced. Key points for Glassdoor:

1.Glassdoor's Terms of Service prohibit automated access. Violating ToS is a civil matter, not criminal, but can result in legal action.
2.The hiQ v. LinkedIn ruling (2022) supports scraping publicly available data, but Glassdoor gates some content behind login walls — scraping authenticated content carries higher legal risk.
3.Salary data aggregated at scale could raise concerns under trade secret law if Glassdoor considers it proprietary.
4.Never scrape personal user data — reviewer identities, email addresses, or private profile information.
5.Rate-limit your requests and respect robots.txt. Consult a lawyer familiar with web scraping case law.

Start free — 100 requests/month

Get your API key in 30 seconds. Scrape Glassdoor data with five lines of code. No credit card, no browser fleet, no proxy bills.

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Frequently asked questions

Glassdoor's Terms of Service prohibit automated scraping. However, the hiQ v. LinkedIn ruling established that scraping publicly available data is not a CFAA violation in the US. Glassdoor reviews, salaries, and interview questions on public pages are generally accessible. Always consult a lawyer for your specific use case and never scrape personal user data.

Glassdoor uses Cloudflare protection, rate limiting, login walls for some data, and browser fingerprinting. SnapRender's use_flaresolverr flag handles Cloudflare challenges automatically. For salary data behind login walls, you may need to authenticate first.

Company ratings, employee reviews, salary ranges by job title, interview questions and difficulty ratings, CEO approval ratings, benefits information, and "Pros vs Cons" review breakdowns. SnapRender's /extract endpoint lets you target any of these with CSS selectors.

SnapRender starts free with 100 requests/month. Paid plans begin at $9/month for 1,500 requests. Each Glassdoor page is one request — no credit multipliers.