Best Glassdoor Scraper Tools and Python Tutorial
Scraping job listings from Glassdoor is challenging due to login walls, overlays, CAPTCHA, and HTML changes. 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.
In this guide, we’ll show how to scrape Glassdoor data using Python and review the best Glassdoor scraper tools that simplify the process. Here’s an overview of the best scrapers for Glassdoor.
Provider | Type of scraper | Starting price/mo | PAYG | Free trial |
|---|---|---|---|---|
Bright Data | Dedicated scraper | $499 for 510,000 requests | $1.5 /1k requests | 7 days |
General job-board scraper | $49+usage for 98,000 requests | ❌ | Free 2,000 credits | |
Dedicated scraper | $19.99+usage | ❌ | 3 days | |
ScraperAPI | General job-board scraper | $49 for 100,000 requests | ❌ | Free 5,000 credits |
ScrapingBee | Universal Scraper (handles Glassdoor) | $49 | ❌ | Free 1,000 credits |
Key points about Glassdoor scraping
- Glassdoor uses aggressive anti-scraping techniques (CAPTCHA, overlays, login requirements).
- Using a Glassdoor scraper API avoids these barriers and returns structured JSON/JSONL data.
- You will learn how to trigger a Glassdoor scraping process programmatically.
- The tutorial includes a polling loop to wait for the scraper to finish.
- Results are saved to a clean CSV file using pandas.
How to scrape Glassdoor reviews and job data 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
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.
The 5 best 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.
There is a separate “Glassdoor company reviews, collected by URL” scraper that pulls employee reviews for a specific company page. For jobs, there are Glassdoor job scrapers that collect job listings by URL, discover new jobs by keyword (e.g., job title), and find jobs via a company URL.
Bright Data also offers three ready-to-use datasets so you can work with pre-collected Glassdoor data instead of scraping it yourself.
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.
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.
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.
💡Conclusion
In this tutorial, we explained how to automate the process of collecting Glassdoor job listings using Python and an external scraping API. Instead of attempting to scrape the site directly, which often triggers CAPTCHA, login requirements, and dynamic content barriers, we relied on an API to handle the heavy lifting and return structured data that is easy to work with.
The web scraping workflow is straightforward: you configure a search request, trigger a scraping job, poll until it completes, download the resulting dataset, and process it into a clean CSV file. Once the data is downloaded, Python takes over entirely. You parsed the returned JSON/JSONL records, extracted key job-related fields, organized them into a DataFrame, and saved them for further use.
This method allows you to focus on analyzing job listings rather than maintaining a fragile HTML scraper. Because scraping is handled externally, you avoid common issues such as IP blocks or UI changes on Glassdoor’s website. The result is a repeatable, dependable workflow that you can adapt for different roles, locations, or additional data types offered by the same dataset.
FAQs about Glassdoor scraping
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