How to Scrape Glassdoor: Top Tools & Python Tutorial

updated on Feb 23, 2026

Glassdoor uses aggressive anti-scraping techniques (CAPTCHA, overlays, login requirements). The moment you load the site, you often encounter login prompts, pop-up overlays, CAPTCHA, and aggressive bot detection.

The page structure also changes frequently, breaking HTML scrapers. Instead of manually circumventing these barriers, we used a managed scraping infrastructure to address them.

Price comparison of the best Glassdoor scrapers

Provider	Type of scraper	Starting price/mo	Free trial
Bright Data	Dedicated scraper	$1.50 / 1k results	7 days
Oxylabs	Job-board scraper	$1.35 / 1k results	2,000 credits
Apify	Dedicated scraper	$19.99	3 days
ScraperAPI	Job-board scraper	$49.00	5,000 credits
ScrapingBee	Job-board scraper	$49.00	1,000 credits

Customers have links and are placed at the top in lists without numerical criteria.

Top 5 Glassdoor scraper APIs

Bright Data Glassdoor scraper lets you extract public data points about company reviews, salaries, and job postings from Glassdoor. They offer ready-made scrapers dedicated to the platform you can run via the Scraper API or the no-code interface.

The Glassdoor scraper collects company profiles directly from the Glassdoor company URL, and helps you discover companies either by input filters, by keyword, or by providing a Glassdoor search URL.

Bright Data has integrated AI-driven DOM discovery into its Glassdoor Scraping API. This feature automatically adapts to Glassdoor’s frequent HTML structure updates.

The provider also offers three ready-to-use datasets so you can work with pre-collected Glassdoor data instead of scraping it yourself.

Oxylabs offers a Job Scraper API for extracting job listing data from Glassdoor pages. Their offering works similarly to ScraperAPI’s approach: they provide a general Job Scraper API that supports multiple job boards (Glassdoor, Indeed, ZipRecruiter) rather than building a dedicated scraper for each site.

This scraper supports any job board, including Glassdoor, because Oxylabs’ Web Scraper API is a Universal Scraping Engine, meaning you pass a target URL (e.g., a Glassdoor job search page), and it handles IP rotation, JavaScript rendering, and anti-bot evasion.

Apify Glassdoor scraper comes with a large set of presets, so you do not have to build every query from scratch. Results can be exported in standard, structured formats such as JSON, CSV, or XLSX.

The tool offers more than forty predefined locations, including remote work plus major global cities such as New York, San Francisco, London, Berlin, and Tokyo, as well as specific countries. It supports advanced filters: you can narrow listings by salary ranges, company rating scores on a 0–5 scale, remote-only positions, and “easy apply” jobs.

There is also a page_offset numeric parameter that sets the starting page for scraping, so you can skip initial pages or resume from a later page; this is labeled as a paid-only feature. Because Glassdoor can be sensitive to scraping, the actor includes proxy configuration options. You can choose between datacenter and residential proxies, or use your own proxies.

In terms of scale, a single run can scrape up to 10,000 job listings. The max_items input parameter lets you cap the number of jobs to collect, and the max_pages parameter enables you to limit the number of result pages the scraper traverses, up to 30 per search query.

ScrapingBee provides a general web scraper applicable for data collection from Glassdoor. Every plan gives you a monthly pool of API credits, and each request consumes credits depending on which features you enable. A basic call with a rotating proxy and no JavaScript rendering uses one credit.

By default, ScrapingBee loads the page in a headless browser, executes its JavaScript, and then returns the fully rendered HTML. This default behaviour costs 5 credits per call when used with standard rotating proxies.

Dedicated scraper APIs are only offered for a few sites (Google Search, Amazon, YouTube, Walmart, ChatGPT), and Glassdoor is not among them, even though the general features you’re seeing are what you would use on sites they do allow.

ScraperAPI doesn’t offer a dedicated Glassdoor-only scraper, unlike Apify or Bright Data. Instead, they offer a broader solution, the Job Board Scraper API, designed to collect job listings and posting data from multiple major job platforms, including LinkedIn, Glassdoor, and Indeed.

This makes their solution more general-purpose and flexible, but less specialized, compared to a focused vendor that maintains Glassdoor-specific endpoints. You send a request to their API specifying the target job board page (URL) or search query. You can enable premium proxies (residential) and set a session_id so multiple requests in the same session reuse the same IP address.

Scrape Glassdoor reviews using Python

Step 1: Setting up your Python environment and API credentials

We begin by importing the required Python libraries, disabling SSL warnings, and defining our search parameters (keyword, location, country) along with your API credentials.

This sets up:

Required libraries
Your API token
Your dataset ID
Search inputs: job keyword, location, country

Step 2: Starting Glassdoor scraping task

Now that the environment is configured, we trigger a scraping job by sending a POST request to the API. If successful, this returns a snapshot_id, which identifies your dataset run.

Step 3: Checking progress and retrieving scraped results

We must poll until the job is marked as:

“ready”
“done”
“complete”

The script waits up to 15 minutes and handles both JSON and JSONL response formats.

Step 4: Processing and CSV export

Once the item list is fully populated, the final step is to convert the job entries into a DataFrame and export them to CSV.

This generates a clean CSV that includes:

Job title
Company name and rating
Location
URLs
Overview text

Glassdoor’s anti-scraping policies and risks

Glassdoor’s Terms of Use explicitly state that you may not¹:

Scrape, strip, or mine any data from the platform.
Use any robot, spider, scraper, or other automated means to access the platform for any purpose without express written permission.
Bypass or circumvent any measures used to prevent or restrict access to the site (e.g., robots.txt, IP blocks, or CAPTCHA).

How to avoid blocks and ensure reliable scraping

Even though this workflow relies on an API rather than direct web scraping, there are still a few essential considerations that help keep your runs error-free. The good news is that much of the reliability is already built into your script.

For example, the polling loop you added includes timed delays, status checks, and a maximum wait period, which prevents the script from hammering the API or getting stuck when a dataset takes longer to process.

One simple practice is to avoid triggering a large number of scraping jobs at once. Each job has to process search parameters such as keywords, country, and location, so it’s better to run them in batches rather than all at once. This makes it easier to track which snapshot is associated with which search and prevents long queues during busy periods.

Your script also handles intermittent delays by checking for 202 responses and waiting before trying again. This is intentional: it gives the backend enough time to finish collecting the data rather than failing immediately or retrying too aggressively.

Another thing your script already does is validate the output. It doesn’t assume that every line of a JSONL response will contain a complete or perfectly formatted item.

Instead, it attempts to parse each line, skips anything that doesn’t decode properly, and then checks whether any usable items were collected. This helps avoid errors when the dataset returns mixed-format responses or partial results.