Best Glassdoor Scrapers: Bright Data, Oxylabs & Decodo

To compare how well different tools handle Glassdoor‘s CAPTCHAs, login overlays, and frequent layout changes, we tested 5 leading web data scrapers across 2,500 requests and tracked each provider’s success rate, completion time, and metadata coverage.

Glassdoor scraping benchmark results

You can read our benchmark methodology for more details on our testing process.

Glassdoor scrapers output format & free trial options

Glassdoor data fields you can scrape

Bright Data was the only provider that returned structured JSON from Glassdoor with 19 fields per job posting.

See the data fields returned for a single Glassdoor job page from Bright Data, grouped into categories:

Top 5 Glassdoor scraper APIs

Bright Data led the Glassdoor benchmark with a 100% success rate. It uses its dedicated Glassdoor Dataset API.

The Glassdoor scraper is available through both the Scraper API and a no-code interface, and beyond job postings, Bright Data also offers dedicated scrapers for company overview data and company reviews.

Oxylabs failed to extract any Glassdoor data. Of the 500 requests:

• 260 returned HTTP 200 with empty/unparseable HTML
• 240 returned HTTP 408 (realtime endpoint timeout on heavy JS pages)

We submitted Glassdoor URLs to Oxylabs’ Web Scraper API using the universal source for IP rotation, JavaScript execution, and bot detection bypass.

Decodo returned no extractable Glassdoor data. Glassdoor URLs went through Decodo’s Web Scraper API with headless: html and proxy_pool: premium. 360 of the 500 requests returned HTTP 400, and the remaining 140 returned HTTP 200 but with no extractable job content. Average completion time before failure was 117 seconds.

Zyte matched Bright Data’s 100% success rate on Glassdoor with the fastest average completion time at 16 seconds. Zyte’s Extract API processed Glassdoor URLs with JavaScript rendering enabled through a headless browser.

Nimble reached a 79% success rate on Glassdoor with an average completion time of 30 seconds. Glassdoor extraction was performed through Nimble’s Web Extract API configured with browser rendering and the vx10 driver. About one in five pages did not render the job-detail DOM elements within the test window, leaving them invalid under our CSS-selector validation.

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).

Glassdoor scraping benchmark methodology

We benchmarked 5 web scraping providers on Glassdoor job posting extraction, with each provider handling the same list of 500 individual job posting URLs. Requests went out sequentially with a 2-second pause in between, producing 2,500 runs in total.

Providers and integration

Bright Data ran through its purpose-built Glassdoor Dataset API , which delivers parsed JSON.

Oxylabs ran through its Web Scraper API with source: universal, returning rendered HTML.

Decodo ran through its Web Scraper API set to headless: html with proxy_pool: premium, also returning rendered HTML.

Nimble ran through its Web Extract API configured with render: true and driver: vx10, producing rendered HTML.

Zyte ran through its Extract API with browserHtml: true, again producing rendered HTML.

When the response was HTML, we ran it through local CSS selectors aimed at Glassdoor’s job-detail elements like h1[id^="jd-job-title-"], .EmployerProfile_employerNameHeading__bXBYr h4, and .JobDetails_badgeStyle__xaoxT[data-test="location"].

Timeout and rate limiting

Async requests had a 10-minute ceiling on execution. If a provider returned HTTP 429, we waited 30 seconds and retried up to 3 times; anything past that was logged as a failure for the URL.

Validation rules

We applied three checks per request.

For submission, the provider had to return an HTTP code in the 200-399 band, or 404. For execution, async jobs (only Bright Data here) had to wrap up before the timeout without errors; sync providers cleared this step automatically. For validation, the response needed to surface either job_title or company_name as a non-empty string. Bright Data’s parsed JSON gave this directly; for HTML responses we relied on CSS selector matches.

We also accepted 404 detections as valid, whether by HTTP code, “page not found” body content, or a provider-specific “dead page” signal, since the provider had correctly flagged a missing listing.

Empty responses without errors got a tentative pass and were revisited at the end: if another provider had pulled real job data from the same URL, the empty response was reclassified as a failure. The flip didn’t apply to 404 detections, which we kept trusted unless another provider’s real data on the same URL contradicted them.

A run only counted as a full success when submission, execution, and validation all cleared.

Metrics measured

Validation success rate captures how many URLs cleared all three checks.

End-to-end completion time is wall-clock time from sending the request to getting a response, in seconds. For Bright Data’s async dataset API, it includes the polling window until the job was ready.

Available metadata fields, for providers returning structured JSON, is the union of unique field names across every response. For HTML providers, the value reflects the fixed set of five CSS selectors we used.

FAQs

Reference Links

1.

Security | Glassdoor

Nazlı Şipi

AI Researcher

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Nazlı is a data analyst at AIMultiple. She has prior experience in data analysis across various industries, where she worked on transforming complex datasets into actionable insights.

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