Glassdoor

Glassdoor Salaries API

An employer's salaries by job title as JSON, ten titles per page: base-pay and total-pay percentiles from P10 to P90 with the medians called out, how many reported salaries each estimate is built from, the currency, the pay period and when the title was last reported on. These are Glassdoor's own estimates for the title, not individual reported salaries. Costs 1 credit per page.

POST/api/v1/glassdoor/salaries

Authorizations

Authorizationstringheaderrequired

Bearer authentication header of the form Bearer <token>, where <token> is your Scavio API key (e.g. Bearer sk_live_your_key).

Body

application/json

employer_idstring

Glassdoor employer id, 1-50 characters, as a STRING (1699, E1699 or IE1699) — a JSON number is rejected. Get one from the Glassdoor Company Search endpoint. Either this or url is required. Addressing by id costs two upstream fetches because the case-sensitive /Salary/ slug has to be read off the profile first; your price is unchanged, but url is the faster path.

Example: 1699

urlstring

Any glassdoor.com employer URL, 1-500 characters. Pass back the salaries_url that the Company Profile endpoint returned to skip the resolve fetch. A non-glassdoor.com host is rejected. Either this or employer_id is required.

Example: https://www.glassdoor.com/Salary/NIKE-Salaries-E1699.htm

pageinteger

Results page, 1-based. Glassdoor fixes the page size at 10 job titles; page_count on the response tells you how many pages exist (a large employer runs to hundreds). Each page is a separate billed request.

Example: 2

companystring

Employer name as it appears in a Glassdoor slug, 1-200 characters. COSMETIC ONLY: ignored entirely when url is set, and it does NOT satisfy the employer_id-or-url requirement.

Example: NIKE

Request

from scavio import ScavioClient

client = ScavioClient(api_key="sk_live_your_key", timeout=180)

# Fastest path: chain on salaries_url from the profile (one upstream fetch)
profile = client.glassdoor.company(employer_id="1699")
page1 = client.glassdoor.salaries(url=profile["data"]["salaries_url"])

for row in page1["data"]["salaries"]:
    print(row["job_title"], row["median_base_pay"], row["salary_count"])

print("pages available:", page1["data"]["page_count"])

Response

"employer_id": 1699,

"name": "NIKE, Inc.",

"short_name": "NIKE",

"url": "https://www.glassdoor.com/Salary/NIKE-Salaries-E1699.htm",

"reviews_url": "https://www.glassdoor.com/Reviews/NIKE-Reviews-E1699.htm",

"salaries_url": "https://www.glassdoor.com/Salary/NIKE-Salaries-E1699.htm",

"location": "United States",

"pay_period": "ANNUAL",

"currency": "USD",

"salary_count": 28229,

"job_title_count": 3908,

"page": 1,

"page_count": 389,

"count": 10,

"response_time": 2408,

"credits_used": 1,

"credits_remaining": 4820

}

from scavio import ScavioClient client = ScavioClient(api_key="sk_live_your_key", timeout=180) # Fastest path: chain on salaries_url from the profile (one upstream fetch) profile = client.glassdoor.company(employer_id="1699") page1 = client.glassdoor.salaries(url=profile["data"]["salaries_url"]) for row in page1["data"]["salaries"]: print(row["job_title"], row["median_base_pay"], row["salary_count"]) print("pages available:", page1["data"]["page_count"])