Glassdoor Jobs Scraper API
for jobs, salaries, and employer ratings
Search Glassdoor jobs and get each listing as clean JSON. One request returns the role, location, required skills, the salary range, and the employer's rating, for any keyword and market, with proxies handled for you.
of requests succeed
median response
95% finish faster
per 1k job searches at volume
Glassdoor changes its markup often. Your parser shouldn't care.
- Job data behind a login wall and captchas
- Salary ranges in mixed shapes
- Sponsored listings mixed into results
- Proxies rotated per locale
- Re-parse listings after each redesign
One GET Request. That's the whole integration.
Start with just a query. Add more parameters when your use case needs them.
curl -G 'https://api.hasdata.com/scrape/glassdoor/listing' \
--data-urlencode 'keyword=software engineer' \
--data-urlencode 'location=New York, NY' \
--data-urlencode 'sort=recent' \
--data-urlencode 'domain=www.glassdoor.com' \
--header 'x-api-key: <YOUR_API_KEY>' \
--header 'Content-Type: application/json'keyword * Search Querylocation * Locationsort Sort BynextPageToken Next Page Tokendomain domaincurl -G 'https://api.hasdata.com/scrape/glassdoor/job' \
--data-urlencode 'url=https://www.glassdoor.com/job-listing/engineering-analyst-anti-scraper-search-google-JV_IC1147431_KO0,39_KE40,46.htm?jl=1009846893500' \
--header 'x-api-key: <YOUR_API_KEY>' \
--header 'Content-Type: application/json'url * Job URLEvery listing, one predictable schema
Each job comes back with the same keys, salary range, skills, and the employer's rating. Map the fields once and forget about it.
[
{
"title": "Software Engineer, Frontend",
"id": 1136043,
"url": "https://www.glassdoor.com/job-listing/software-engineer-frontend-JV_IC1132348.htm",
"location": "New York, NY",
"ageInDays": 152,
"sponsored": false,
"skills": ["React", "TypeScript", "Tooling"],
"salary": {
"currency": "USD",
"period": "ANNUAL",
"min": 143200,
"median": 213600,
"max": 284000,
"source": "EMPLOYER_PROVIDED"
},
"employer": {
"name": "Ramp Network",
"rating": 3.3,
"logoUrl": "https://media.glassdoor.com/sql/7308535/ramp-network-squareLogo.png"
}
}
]title / location stringRole title and where it's based
id / url number / stringGlassdoor job ID and listing URL
ageInDays / sponsored number / booleanDays since posting and paid-placement flag
skills string[]Skills the listing calls for
salary.min / median / max numberSalary range with the median
salary.period / source stringANNUAL or HOURLY, and where the figure came from
employer.name / rating string / numberHiring company and its Glassdoor rating
// The job endpoint: full detail for one listing by URL, including the HTML description.
{
"title": "Software Engineer, Frontend",
"employer": { "name": "Ramp", "industry": "Financial Services" },
"address": { "city": "New York", "region": "New York State", "country": "United States", "latitude": 40.71417, "longitude": -74.00639 },
"salary": { "period": "ANNUAL", "min": 143000, "median": 214000, "max": 284000, "source": "EMPLOYER_PROVIDED" },
"requirements": { "experienceRequirements": ["React", "TypeScript"], "monthsOfExperience": 0 },
"datePosted": "2026-07-25T00:00:00",
"validThrough": "2026-11-25",
"expired": false
}title stringRole title
employer objectCompany name, industry, and logo
address objectCity, region, country, and coordinates
salary.min / median / max numberPay range with the median
requirements objectRequired skills and months of experience
datePosted / validThrough / expired string / booleanPosting and expiry dates
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We rely on HasData for search performance data and broader scraping needs. Their APIs deliver highly structured data that integrates directly into our platforms.
Great web scraping API which is incredibly easy to use. It requires minimal effort to get up and running, and the documentation is very clear and helpful.
I needed to scrape some information they didn't already support, and they wrote the code for me right away, which was super nice of them.
We were particularly impressed with how easily we could integrate HasData into our existing workflow.
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Questions, answered
Both. The listing endpoint takes a keyword and location and returns matching jobs. The job endpoint takes a listing URL and returns one role in full, with the HTML description and benefits. Same field names either way.
When Glassdoor shows it, yes. Each listing carries a salary object with min, median, and max, the period (annual or hourly), and whether the figure is employer-provided or estimated.
From Python or any language. It's a plain HTTP GET with your API key, so requests, axios, or curl all work. Ready-made snippets are in the docs.
Per successful request, whether it's a search page or a single job. A failed request costs nothing.
Yes, a one-time trial of 100 job searches with every field included. No credit card required. When you're done testing, pick a plan that fits your volume.
One job search costs 10 credits. The unit price drops with volume, from about $2.45 down to $0.75 per 1,000 searches. Need more than the top plan covers? We'll set a custom rate.
No. Requests run on HasData's infrastructure, so there's nothing to provision or maintain. You're responsible for using the results in line with each target site's terms and applicable law.
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