Glassdoor Scraper API — Jobs, Companies, Salaries & Reviews Data

A high-performance Glassdoor scraper that extracts complete job listings from Glassdoor.com, one of the most trusted job & employer review platforms with 80M+ reviews and millions of active job postings across the US, UK, Canada, India, Australia, Germany and France. Pull 25+ structured fields per job — title, company name, location parsed to city/state/country, salary range with currency and unit, full description, employment type, posting date, and education/experience requirements — without writing a single line of HTML scraping logic.

Built for recruiters tracking US, UK and EMEA labor supply, market analysts comparing pay across LinkedIn Jobs, Indeed, and BLS data, salary benchmarking firms, founders monitoring competitor hiring patterns, and ML teams training resume-to-job matching pipelines against the schema.org JobPosting schema. Output is clean JSON, ready for Google BigQuery, Snowflake, Postgres, or Google Sheets.

What this Glassdoor scraper does

This actor automates browsing glassdoor.com the way a real visitor does. It hits the public job search pages, walks through pagination, parses the embedded schema.org JobPosting JSON-LD blocks served alongside each search result, and visits every individual job detail page to extract the full structured posting — title, company, salary, location, employment type, description, education and experience requirements.

The pricing logic is automatic: Glassdoor's JSON-LD baseSalary block becomes salaryMin: 143913, salaryMax: 197200, salaryCurrency: "USD", salaryUnit: "YEAR", plus a human-readable salaryText: "$143,913 - $197,200 USD/year". Locations are split into city, state/region, country, postal code and street address, so downstream filtering by city or state works out of the box.

The scraper is HTTP-only — no headless browser, no captcha solver. Memory footprint is 512 MB, dramatically lower than the 4 GB needed by browser-based competitors. It runs on Apify's residential proxy network out of the box.

Data fields the Glassdoor scraper extracts

Every record contains these fields when present in Glassdoor's structured data:

Identity & links

• title — Position title
• jobUrl — Direct link to the Glassdoor listing
• position — Position in the search results ranking

Company

• companyName — Hiring company
• companyUrl — Company's external URL (sameAs from JSON-LD)
• companyLogoUrl — Logo image URL
• industry — Industry classification

Location

• location — Concatenated "City, State, Country" string
• addressLocality — City
• addressRegion — State or region
• addressCountry — Country name
• postalCode — Postal/ZIP code
• streetAddress — Street address when published
• jobLocationType — "TELECOMMUTE" when remote
• applicantLocationRequirements — Geographic eligibility

Salary

• salaryText — Human-readable formatted string
• salaryMin — Lower bound number
• salaryMax — Upper bound number
• salaryValue — Single value when no range is given
• salaryCurrency — "USD", "EUR", "GBP", etc.
• salaryUnit — "YEAR", "HOUR", "MONTH", "WEEK"

Description & requirements

• jobDescription — Full description text
• jobDescriptionHtml — Original HTML markup of the description
• qualifications — Required qualifications when separately published
• responsibilities — Day-to-day responsibilities
• skills — Skills list from JSON-LD when provided
• educationRequirements — Education level required
• experienceRequirements — Experience required

Timestamps & employment type

• datePosted — ISO 8601 posting date
• postedDate — Alias of datePosted
• validThrough — Listing expiration date
• employmentType — Array of types ("FULL_TIME", "PART_TIME", "CONTRACTOR", "INTERN")
• directApply — Boolean: can apply directly on Glassdoor?

Use cases for this Glassdoor data API

1. Recruitment talent radar — Track jobs matching your hiring niche, filter by addressRegion for US states, by employmentType for full-time only, by salaryMin for compensation bands.
2. Salary benchmarking — Aggregate salaryMin/salaryMax by job title + location to produce reliable pay bands. The salaryUnit field lets you cleanly separate hourly vs annual data.
3. Hiring intelligence — Track which companies are scaling specific functions. Combine companyName + industry for clean B2B segmentation.
4. ML training corpora — Pair title, full jobDescription, educationRequirements, and experienceRequirements to train resume-to-job matching models.
5. Lead generation — Identify companies actively hiring in your niche (e.g. all US companies hiring a "VP of Engineering"). Use companyName + addressLocality for geographic prospecting.
6. Compensation transparency reports — Detect employers consistently posting salary ranges vs those who don't, useful for pay equity research.

How to use this Glassdoor scraper

Mode 1 — Search by keywords (easiest)

{
  "mode": "keywords",
  "keywords": ["python developer", "data engineer"],
  "country": "us",
  "maxJobs": 100,
  "fetchDetails": true
}

Mode 2 — Multi-country pipeline

{
  "mode": "keywords",
  "keywords": ["software engineer"],
  "country": "uk",
  "maxJobs": 200,
  "fetchDetails": true
}

Mode 3 — Pre-built search URLs (advanced)

{
  "mode": "urls",
  "searchUrls": [
    { "url": "https://www.glassdoor.com/Job/remote-python-developer-jobs-SRCH_IL.0,6_IS11047_KO7,23.htm" }
  ],
  "maxJobs": 50,
  "fetchDetails": true
}

Input parameters

Parameter	Type	Default	Description
mode	enum	"keywords"	"keywords" or "urls"
keywords	array	["python developer"]	Job titles / keywords
country	enum	"us"	Regional Glassdoor domain
searchUrls	array	[]	Pre-built URLs (urls mode)
maxJobs	integer	50	Cap per keyword/URL
fetchDetails	boolean	true	Visit detail pages for full structured data
proxyConfiguration	object	RESIDENTIAL	Apify proxy (residential recommended)

Output example (single record, JSON)

{
  "position": 1,
  "title": "Python Developer",
  "jobUrl": "https://www.glassdoor.com/job-listing/python-developer-social-security-administration-...",
  "companyName": "US Social Security Administration",
  "companyLogoUrl": "https://media.glassdoor.com/sql/...png",
  "companyUrl": "https://www.ssa.gov",
  "industry": "Government",
  "location": "Woodlawn, Maryland, United States",
  "addressLocality": "Woodlawn",
  "addressRegion": "Maryland",
  "addressCountry": "United States",
  "salaryText": "$143,913 - $197,200 USD/year",
  "salaryMin": 143913,
  "salaryMax": 197200,
  "salaryCurrency": "USD",
  "salaryUnit": "YEAR",
  "employmentType": ["FULL_TIME"],
  "datePosted": "2026-05-06T00:00:00",
  "validThrough": "2026-06-06T00:00:00",
  "jobDescription": "Summary IT Specialist (APPSW), Python Developer position(s) are being filled through the Office of Personnel Management...",
  "educationRequirements": "Bachelor's degree",
  "experienceRequirements": "5 years"
}

How to call this Glassdoor scraper from your code

From the Apify API (curl)

curl -X POST "https://api.apify.com/v2/acts/makework36~glassdoor-scraper/run-sync-get-dataset-items?token=$APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "mode": "keywords",
    "keywords": ["data engineer"],
    "country": "us",
    "maxJobs": 100,
    "fetchDetails": true
  }'

From Python (apify-client)

from apify_client import ApifyClient
client = ApifyClient("YOUR_APIFY_TOKEN")
run = client.actor("makework36/glassdoor-scraper").call(run_input={
    "mode": "keywords",
    "keywords": ["python developer", "data scientist"],
    "country": "us",
    "maxJobs": 200,
    "fetchDetails": True,
})
for job in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(job["title"], job["companyName"], job.get("salaryText"))

From Node.js (apify-client)

import { ApifyClient } from 'apify-client';
const client = new ApifyClient({ token: 'YOUR_APIFY_TOKEN' });
const run = await client.actor('makework36/glassdoor-scraper').call({
    mode: 'keywords',
    keywords: ['software engineer'],
    country: 'us',
    maxJobs: 100,
    fetchDetails: true,
});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(`Got ${items.length} jobs`);

Export to CSV

curl "https://api.apify.com/v2/acts/makework36~glassdoor-scraper/runs/last/dataset/items?format=csv&token=$APIFY_TOKEN" \
  -o glassdoor_jobs.csv

Performance & cost

Mode	Time per job	100 jobs ETA
Search only (titles + URLs)	~0.5 s	~50 s
With fetchDetails: true	~7–15 s	~12–25 min

Detailed mode visits each individual job page to extract the full JSON-LD JobPosting. This is necessary for salary, full description, employment type and address. Search-only mode is much faster but returns only title + URL.

Glassdoor scraper comparison

Feature	This actor	Browser-only with proxy	Headless + captcha solver
Output fields	25+ structured	15–20	18
Salary parsed to Min/Max/Currency/Unit	✅	partial	partial
Location split (city/state/country)	✅	❌	❌
Pure HTTP (no browser)	✅	❌	❌
Memory footprint	512 MB	4 GB	4 GB
Captcha solver required	❌	❌	✅
Pricing model	PAY_PER_EVENT tiered	flat	flat

Step-by-step tutorial — your first run in 2 minutes

1. Click Try for free at the top of this page.
2. Leave mode as keywords and country as us.
3. Change the default keyword (e.g. "data analyst") and adjust maxJobs to 20 for a quick test.
4. Click Start. The run finishes in ~3–5 minutes (detailed mode).
5. Open the dataset; you'll see a table with titles, companies, locations, salaries.
6. Export as CSV, JSON or Excel from the Export button.
7. Once happy, scale maxJobs, schedule recurring runs, or hook into your data pipeline.

Advanced usage patterns

Pattern A — Daily talent radar by US region

Schedule the actor daily with keywords: ["your role"] + country: "us". Use the addressRegion field to slice results by state.

Pattern B — Multi-country compensation atlas

Run with the same keyword set across country: "us", "uk", "de", "in". Aggregate salaryMin/salaryMax by country + role.

Pattern C — Remote-only feed

Filter outputs where jobLocationType == "TELECOMMUTE" post-extract. Combine with salaryMin >= 100000 to build a high-quality remote tech feed.

Pattern D — Education requirement tracker

Group by educationRequirements over time. Detect roles shifting from "Bachelor's" toward "Master's" requirements as competition intensifies.

Pattern E — Competitor hiring tracker

Use Glassdoor's URL filter for a specific company name (mode: urls). Track posting velocity over weeks to detect headcount growth.

Troubleshooting

Some salary fields are null. Not every Glassdoor posting includes salary. About 50–60% of US listings have salary in the JSON-LD; the rest return null. The structured fields are populated only when the employer publishes them.

Run takes longer than expected. Detail mode adds ~7–15 s per job because each individual page is fetched and parsed. For 1,000 jobs in detailed mode, expect 2–4 hours. Run search-only mode first for a fast title-level scan, then re-run only on interesting jobs in detailed mode.

HTTP 403 errors in the logs. Normal — Glassdoor rate-limits each IP. The actor rotates Apify residential proxy sessions and retries automatically. As long as the final job count is non-zero, the scraper recovered.

My run hit a proxy budget limit. Each search page is ~500 KB and each detail page ~80 KB. For 1,000 jobs (~30 search pages + 1,000 detail pages) expect ~95 MB of proxy traffic. Check your Apify residential allowance.

Results show only title and jobUrl. You ran with fetchDetails: false. Enable it to extract the full structured data from each job's individual page.

I want only jobs from one specific country. Set country to us, uk, ca, in, au, de, or fr. The scraper uses the matching regional Glassdoor domain.

Pricing

This actor uses PAY_PER_EVENT pricing with tiered volume discounts.

Tier	Standard job (URL+title only)	Detailed job (full JSON-LD)
FREE	$1.50 / 1K	$7.50 / 1K
BRONZE	$1.10 / 1K	$5.50 / 1K
SILVER	$0.80 / 1K	$4.00 / 1K
GOLD	$0.55 / 1K	$2.80 / 1K
PLATINUM	$0.40 / 1K	$2.20 / 1K
DIAMOND	$0.28 / 1K	$1.80 / 1K

Plus a one-time $0.001 per actor run. Detailed mode is the default because Glassdoor's value lives in the structured JobPosting data on each individual page.

FAQ

Why does the scraper need Residential proxies? Glassdoor blocks datacenter IPs aggressively. Residential proxies route traffic through real households, which Glassdoor accepts. Apify includes residential proxy access in most paid plans.

Is scraping Glassdoor legal? This actor accesses publicly available job pages — no login, no paywall bypass, no PII extraction beyond what employers post publicly. Always check Glassdoor's terms of service and consult legal counsel for your jurisdiction.

Can I scrape Glassdoor company reviews / salaries? No — this actor focuses on job listings. Glassdoor's reviews and aggregated salary reports require a different stack and have different terms-of-service implications.

Does this support Glassdoor UK / Canada / Germany / France? Yes. Set country to uk, ca, de, fr, in, or au and the scraper hits the matching regional Glassdoor domain.

How fresh is the data? The scraper fetches in real time at run start. Glassdoor updates listings continuously.

Can I scrape Glassdoor without writing code? Yes — use the input form on this page, click Start, then download the dataset as CSV or Excel.

Why are qualifications and responsibilities fields sometimes empty? Not all Glassdoor postings split these out as separate JSON-LD properties. When absent, the same information typically lives inside jobDescription as a paragraph.

Does the scraper deduplicate jobs? Yes. Each jobUrl is tracked across pages within a run; duplicates are skipped.

Is there a rate limit on my side? The actor uses jitter between pages and rotates proxy sessions automatically. For very large runs (>5,000 jobs), split across multiple Apify Tasks scheduled minutes apart.

Changelog

• 0.1 (2026-05) — Initial release. Keywords + URLs modes. JSON-LD parsing for search and detail. 25+ fields. Residential proxy default.

Related scrapers from the same author

• makework36/indeed-scraper — Indeed.com US jobs
• makework36/naukri-scraper — Naukri.com India jobs
• makework36/trustpilot-reviews-scraper — Trustpilot reviews
• makework36/google-maps-scraper-full — Google Maps places

Legal & ethics note

This Glassdoor scraper accesses publicly available information. It does not bypass authentication, paywalls, or extract personally identifiable information beyond what employers and Glassdoor display publicly. Users are responsible for complying with Glassdoor's terms of service, the Computer Fraud and Abuse Act, and any data protection regulations applicable in their jurisdiction (e.g. CCPA, GDPR). The author of this actor accepts no responsibility for misuse.