Glassdoor Review Scraper Python: Build a Working Tool in 30 Minutes

Glassdoor is a goldmine of valuable data—employee reviews, company ratings, salary ranges, and interview insights. For job seekers, it’s a tool to research potential employers; for businesses, it’s a way to analyze competitors or monitor their own reputation; for data analysts, it’s a rich dataset for trend analysis. But here’s the problem: manually collecting this data is tedious, time-consuming, and prone to error.

This is where a Glassdoor review scraper Python comes in. Python is the perfect language for web scraping—thanks to its lightweight libraries (requests, BeautifulSoup, Selenium) and easy-to-read syntax. A custom Python scraper lets you automate the process of extracting Glassdoor reviews at scale, saving you hours of manual work.

But there’s a catch: Glassdoor has strict anti-scraping measures. If you send too many requests from a single IP address, you’ll get blocked—ending your scraping workflow abruptly. This is the biggest pain point for anyone building a Glassdoor review scraper. The solution? Use a reliable proxy service to rotate IP addresses and avoid detection.

In this guide, we’ll walk you through building a fully functional Glassdoor review scraper with Python. We’ll cover everything: setting up your environment, writing the core scraping code, handling dynamic content, and—most importantly—integrating a proxy (IPFLY, a client-free, high-availability proxy) to bypass Glassdoor’s IP bans. By the end, you’ll have a scraper that can extract reviews safely and efficiently, with code you can copy-paste and customize.

What You Need to Know Before Scraping Glassdoor

Before diving into code, let’s cover a few critical prerequisites to avoid issues:

1. Legal & Ethical Considerations

Glassdoor’s Terms of Service prohibit unauthorized scraping. Always: 1) Scrape only public data (avoid private information like employee contact details). 2) Limit your request rate (don’t overload Glassdoor’s servers). 3) Use the data for personal/educational purposes (commercial use may require Glassdoor’s permission). 4) Respect robots.txt (check https://www.glassdoor.com/robots.txt for restricted pages).

2. Glassdoor’s Anti-Scraping Measures

Glassdoor uses several anti-scraping techniques to block bots. Your scraper needs to bypass these:

IP Blocking: The most common—sending multiple requests from one IP triggers a ban.

User-Agent Detection: Bots with generic User-Agents are flagged (use a real browser’s User-Agent).

Dynamic Content: Many reviews are loaded dynamically with JavaScript (requires tools like Selenium or Playwright).

CAPTCHAs: Rare for low-volume scraping, but may appear if you’re detected (solved with proxy rotation).

3. Tools & Libraries You’ll Need

Install these Python libraries before starting (use pip install [library]):

• requests: For sending HTTP requests to Glassdoor.
• BeautifulSoup4: For parsing HTML and extracting data.
• selenium: For handling dynamic JavaScript content (critical for Glassdoor).
• pandas: For storing scraped reviews in a CSV/Excel file.
• webdriver-manager: For managing Selenium browser drivers (no manual downloads).

Build a Basic Glassdoor Review Scraper with Python

We’ll start with a basic scraper that extracts reviews from a single Glassdoor company page. This scraper uses Selenium to handle dynamic content (since Glassdoor loads reviews via JavaScript) and BeautifulSoup for parsing.

Step 1: Import Required Libraries

from selenium import webdriver
from selenium.webdriver.chrome.service import Service
from webdriver_manager.chrome import ChromeDriverManager
from bs4 import BeautifulSoup
import pandas as pd
import time

Step 2: Configure Selenium & Basic Scraper Setup

def init_driver():
    # Configure Chrome options to mimic a real browser
    chrome_options = webdriver.ChromeOptions()
    chrome_options.add_argument("--disable-blink-features=AutomationControlled")
    chrome_options.add_argument("--user-agent=Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/129.0.0.0 Safari/537.36")
    chrome_options.add_experimental_option("excludeSwitches", ["enable-automation"])
    chrome_options.add_experimental_option("useAutomationExtension", False)
    
    # Initialize Chrome driver
    driver = webdriver.Chrome(
        service=Service(ChromeDriverManager().install()),
        options=chrome_options
    )
    driver.implicitly_wait(10)  # Wait 10s for elements to load
    return driver

def scrape_glassdoor_reviews(driver, company_url, num_pages=1):
    # Store scraped data
    reviews_data = []
    
    for page in range(1, num_pages + 1):
        # Navigate to the company reviews page (with page number)
        page_url = f"{company_url}?page={page}"
        driver.get(page_url)
        time.sleep(2)  # Wait for page to load (adjust if needed)
        
        # Parse page source with BeautifulSoup
        soup = BeautifulSoup(driver.page_source, "html.parser")
        
        # Find all review containers (inspect Glassdoor's HTML to get the correct class)
        review_containers = soup.find_all("div", class_="gdReview")
        
        if not review_containers:
            print(f"No reviews found on page {page}. Exiting...")
            break
        
        # Extract data from each review
        for review in review_containers:
            try:
                # Review title
                title = review.find("h2", class_="reviewTitle").text.strip() if review.find("h2", class_="reviewTitle") else "N/A"
                
                # Rating (1-5 stars)
                rating = review.find("span", class_="ratingNumber").text.strip() if review.find("span", class_="ratingNumber") else "N/A"
                
                # Review text
                review_text = review.find("div", class_="reviewText").text.strip() if review.find("div", class_="reviewText") else "N/A"
                
                # Author details (job title, location)
                author_details = review.find("span", class_="authorInfo").text.strip() if review.find("span", class_="authorInfo") else "N/A"
                
                # Date of review
                date = review.find("time", class_="date")["datetime"] if review.find("time", class_="date") else "N/A"
                
                # Add to data list
                reviews_data.append({
                    "Title": title,
                    "Rating": rating,
                    "Review Text": review_text,
                    "Author Details": author_details,
                    "Date": date
                })
            except Exception as e:
                print(f"Error extracting review: {str(e)}")
                continue
        
        print(f"Scraped {len(review_containers)} reviews from page {page}")
    
    return reviews_data

Step 3: Run the Scraper & Save Data

if __name__ == "__main__":
    # Initialize driver
    driver = init_driver()
    
    # Example: Glassdoor company reviews URL (replace with your target)
    target_company_url = "https://www.glassdoor.com/Reviews/Google-Reviews-E9079"
    
    # Scrape 3 pages of reviews
    scraped_reviews = scrape_glassdoor_reviews(driver, target_company_url, num_pages=3)
    
    # Save data to Excel
    if scraped_reviews:
        df = pd.DataFrame(scraped_reviews)
        df.to_excel("glassdoor_google_reviews.xlsx", index=False)
        print(f"Successfully saved {len(scraped_reviews)} reviews to glassdoor_google_reviews.xlsx")
    else:
        print("No reviews scraped.")
    
    # Close the driver
    driver.quit()

The Big Problem: Glassdoor IP Bans & How to Fix Them

If you run the basic scraper above for more than 5–10 pages, you’ll likely get an IP ban. Glassdoor detects frequent requests from a single IP and blocks it—you’ll see a “403 Forbidden” error or be redirected to a CAPTCHA page. This is a showstopper for large-scale scraping.

The only reliable fix is to use a proxy service to rotate IP addresses. A proxy routes your requests through a different IP, making it look like the requests are coming from multiple users (not a single bot). But not all proxies work for Glassdoor scraping—here’s what to avoid:

Free proxies: Slow, unstable, and often already blocked by Glassdoor. They’ll cause your scraper to fail or get banned faster.

Client-based VPNs: Require installing software, which is hard to integrate with Selenium/Python scrapers. They also use static IPs (not rotated) and break automation.

Low-quality paid proxies: High downtime, slow speeds, and shared IPs (overused by other scrapers). They’ll lead to inconsistent results and frequent bans.

For Glassdoor review scrapers, you need a client-free, high-availability proxy service that supports IP rotation and integrates seamlessly with Python/Selenium. This is where IPFLY excels.

Integrate IPFLY Proxy into Your Glassdoor Review Scraper

IPFLY is a client-free proxy service designed for web scraping. With 99.99% uptime, 100+ global nodes, and simple integration with Selenium/Python, IPFLY lets you rotate IPs effortlessly—avoiding Glassdoor’s IP bans. Best of all: no software installation required—just add a few lines of code to your scraper.

Key IPFLY Advantages for Glassdoor Scraping

100% Client-Free Integration: Works directly with Selenium’s proxy settings—no extra software to install. Perfect for Python scrapers running on local machines or cloud servers (headless environments).

99.99% Uptime: IPFLY’s global nodes are optimized for web scraping, ensuring no dropped connections or downtime—critical for long-running scrapers (e.g., scraping 100+ pages of reviews).

IP Rotation: Rotate IPs with every request or page load to mimic real user behavior. Glassdoor won’t detect your scraper as a bot.

Fast Speeds: Low latency (average 50–150ms) ensures your scraper runs quickly—no waiting for slow proxies.

Global Coverage: Access Glassdoor regions (e.g., Glassdoor US, Glassdoor UK) by choosing an IPFLY node in the target country—ideal for scraping region-specific reviews.

Step-by-Step: Add IPFLY to Your Python Scraper

Update the init_driver() function to include IPFLY’s proxy settings. Here’s how:

def init_driver_with_ipfly():
    # IPFLY Proxy Configuration (replace with your details from IPFLY dashboard)
    IPFLY_USER = "your_ipfly_username"
    IPFLY_PASS = "your_ipfly_password"
    IPFLY_IP = "198.51.100.50"
    IPFLY_PORT = "8080"
    
    # Configure Chrome options with IPFLY proxy
    chrome_options = webdriver.ChromeOptions()
    chrome_options.add_argument("--disable-blink-features=AutomationControlled")
    chrome_options.add_argument("--user-agent=Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/129.0.0.0 Safari/537.36")
    chrome_options.add_experimental_option("excludeSwitches", ["enable-automation"])
    chrome_options.add_experimental_option("useAutomationExtension", False)
    
    # Add IPFLY proxy to Chrome options
    proxy = f"{IPFLY_IP}:{IPFLY_PORT}"
    chrome_options.add_argument(f'--proxy-server=http://{proxy}')
    
    # Initialize Chrome driver
    driver = webdriver.Chrome(
        service=Service(ChromeDriverManager().install()),
        options=chrome_options
    )
    
    # Handle proxy authentication (if required)
    driver.get(f"http://{IPFLY_USER}:{IPFLY_PASS}@{IPFLY_IP}:{IPFLY_PORT}")
    time.sleep(2)
    
    driver.implicitly_wait(10)
    return driver

Updated Scraper with IPFLY

if __name__ == "__main__":
    # Initialize driver with IPFLY proxy (replace init_driver() with this)
    driver = init_driver_with_ipfly()
    
    # Target company URL (replace with your own)
    target_company_url = "https://www.glassdoor.com/Reviews/Google-Reviews-E9079"
    
    # Scrape 10 pages of reviews (safe with IPFLY proxy)
    scraped_reviews = scrape_glassdoor_reviews(driver, target_company_url, num_pages=10)
    
    # Save to Excel
    if scraped_reviews:
        df = pd.DataFrame(scraped_reviews)
        df.to_excel("glassdoor_google_reviews_ipfly.xlsx", index=False)
        print(f"Successfully saved {len(scraped_reviews)} reviews (IPFLY proxy used)")
    else:
        print("No reviews scraped.")
    
    driver.quit()

IPFLY vs. Other Proxies for Glassdoor Scraping: Data-Driven Comparison

We tested IPFLY against common proxy types with the Glassdoor review scraper, measuring key metrics for scrapers: success rate, pages scraped before ban, and speed. Here are the results (scraping 50 pages of reviews):

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Advanced Optimization for Your Glassdoor Review Scraper

Take your scraper to the next level with these advanced tips:

1. Handle Pagination Automatically

Instead of specifying a fixed number of pages, modify the scraper to keep scraping until there are no more reviews:

def scrape_all_reviews(driver, company_url):
    reviews_data = []
    page = 1
    
    while True:
        page_url = f"{company_url}?page={page}"
        driver.get(page_url)
        time.sleep(2)
        
        soup = BeautifulSoup(driver.page_source, "html.parser")
        review_containers = soup.find_all("div", class_="gdReview")
        
        if not review_containers:
            print("No more reviews found. Exiting...")
            break
        
        # Extract reviews (same as before)
        for review in review_containers:
            # ... (extraction code)
            pass
        
        print(f"Scraped {len(review_containers)} reviews from page {page}")
        page += 1
    
    return reviews_data

2. Scrape Additional Data (Salaries, Interviews)

Modify the scraper to extract more data (e.g., salary ranges, interview questions) by updating the extraction logic. For example, to scrape salaries:

# Add this to the review extraction loop (if available)
salary = review.find("span", class_="salaryAmount").text.strip() if review.find("span", class_="salaryAmount") else "N/A"

3. Run the Scraper Headless (No Browser Window)

For cloud/server environments, run Selenium in headless mode (no visible browser window):

chrome_options.add_argument("--headless=new")  # Add this to Chrome options

4. Add Request Delays & Retries

Avoid overwhelming Glassdoor’s servers by adding random delays between requests. Use the random library:

import random

# Replace time.sleep(2) with:
time.sleep(random.uniform(1.5, 3.5))  # Random delay between 1.5–3.5 seconds

Common Glassdoor Scraper Issues & Fixes (IPFLY Focused)

Even with IPFLY, you may run into issues. Here are the most common problems and their solutions:

Issue 1: Scraper Gets Blocked Even with IPFLY

Fix: 1) Increase the request delay (use random.uniform(3, 5)). 2) Rotate IPs more frequently (fetch new IPFLY nodes for each page). 3) Update your User-Agent (use a list of real User-Agents and randomize them).

Issue 2: Proxy Authentication Failed

Fix: 1) Verify your IPFLY username/password/IP/port are correct (check your IPFLY dashboard). 2) URL-encode special characters in your password (e.g., @ → %40).

Issue 3: Slow Scraping Speed

Fix: 1) Use an IPFLY node closer to Glassdoor’s servers (e.g., US node for Glassdoor US). 2) Reduce the request delay (but don’t go below 1.5 seconds). 3) Disable unnecessary Chrome options (e.g., image loading):

chrome_options.add_argument("--blink-settings=imagesEnabled=false")  # Disable images

Issue 4: Dynamic Content Not Loading

Fix: 1) Increase Selenium’s implicit wait time (e.g., driver.implicitly_wait(15)). 2) Use explicit waits for specific elements:

from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC

# Wait for reviews to load
WebDriverWait(driver, 15).until(
    EC.presence_of_element_located((By.CLASS_NAME, "gdReview"))
)

Frequently Asked Questions About Glassdoor Review Scraper Python

Q1: Can I scrape Glassdoor without Selenium?

It’s difficult. Glassdoor loads reviews dynamically with JavaScript, which requests/BeautifulSoup can’t parse (they only fetch static HTML). Selenium or Playwright is required to render dynamic content.

Q2: How to avoid CAPTCHAs when scraping Glassdoor?

Use IPFLY to rotate IPs, add random request delays, and mimic real user behavior (e.g., random User-Agents, scrolling). For frequent CAPTCHAs, use a CAPTCHA-solving service (e.g., 2Captcha) or reduce your request rate.

Q3: Why is IPFLY better than free proxies for Glassdoor scraping?

Free proxies are slow, unreliable, and often blocked by Glassdoor. IPFLY’s 99.99% uptime, IP rotation, and fast speeds ensure your scraper runs smoothly without bans. It also integrates seamlessly with Selenium—no extra setup.

Q4: Can I scrape Glassdoor reviews in bulk (1000+ reviews)?

Yes—with IPFLY. Use IP rotation, request delays, and headless mode to scrape in bulk. For very large datasets, consider using IPFLY’s enterprise plan (unlimited IPs) and distribute the scraper across multiple threads (use concurrent.futures).

Q5: Is it legal to scrape Glassdoor reviews?

Glassdoor’s Terms of Service prohibit unauthorized scraping. Always scrape public data, limit your request rate, and use the data for non-commercial purposes. Consult a legal professional if you’re unsure.

Build a Reliable Glassdoor Review Scraper with Python & IPFLY

A Glassdoor review scraper Python is a powerful tool for extracting valuable employer data—but Glassdoor’s anti-scraping measures make it challenging. The key to success is using a reliable proxy service like IPFLY to avoid IP bans.

In this guide, we’ve covered everything you need to build a working scraper: environment setup, core code, dynamic content handling, and IPFLY proxy integration. With the code and tips provided, you can customize the scraper to extract reviews, salaries, or interview data for any company—safely and efficiently.

Ready to start scraping? Sign up for IPFLY’s free trial, copy the code from this guide, and replace the target company URL with your own. You’ll be extracting Glassdoor reviews in no time—without worrying about IP bans.