Glassdoor Reviews & Jobs Scraper

🚀 What is Glassdoor Reviews & Jobs Scraper?

Glassdoor Reviews & Jobs Scraper is an Apify Actor that works as a Glassdoor reviews scraper. It collects company profiles, employee reviews, interview reports, salaries and job listings from Glassdoor for the companies you name. The Actor sets no review limit of its own, and it needs no Glassdoor login or API key.

• Five kinds of data in one run: a company profile, reviews, interviews, salaries and jobs, each saved in its own table.
• No review limit of its own: leave Max reviews at 0 to collect every review the run can reach, or set a number per company.
• Mixed input: company names, Glassdoor links from any country or tab, and numeric employer IDs can share one list.
• Live results: rows appear in the Output tab while the run is going, and the log counts them as they are saved.
• Easy export: download JSON, CSV, Excel, XML or HTML, or read the dataset through the API.

📊 What data can you extract from Glassdoor?

Every row in the main table has a type: company, review, interview, salary, job or error. The Output tab also shows one table per type, so each kind of data has its own columns. A field that Glassdoor does not show for a row stays null. The tables below list the main fields; the dataset schema has a few more.

The company rows hold glassdoor company data such as size, industry and ratings.

Company profile (type = company)

Field	Description	Example
company_id, company_name, glassdoor_url, website	Employer ID, name, overview page and company website	Example Corp
headquarters, size, employees_min, employees_max, industry, sector, company_type, revenue, founded	Basic company facts	1001 to 5000 Employees
ceo_name, description, mission	CEO and company text	text
rating_overall, rating_culture_values, rating_diversity_inclusion, rating_work_life_balance, rating_senior_management, rating_compensation_benefits, rating_career_opportunities	Overall rating and six sub-ratings, out of 5	3.8
recommend_to_friend_pct, ceo_approval_pct, business_outlook_positive_pct	Share of reviewers who recommend, approve of the CEO or expect a positive outlook	68.0
review_count, review_count_by_language, reviews_fetched_by_language, reviews_last_7_days, reviews_last_30_days	Review totals, per language, and what this run collected	1620
interview_count, interview_difficulty_avg, interview_experience, interview_source	Interview totals and averages	3.17
salary_job_title_count, salary_country, job_count, photo_count	Salary, job and photo counts	688
review_insights	Simple counts and word frequencies calculated from the reviews your run collected	object
scraped_at	When the row was collected (UTC)	2025-06-12T10:00:00+00:00

The fields stock_symbol, salary_count_total and industry_benchmark can be empty.

The seven rating fields are glassdoor ratings data out of 5. Together with the profile fields they make a company ratings dataset.

Reviews (type = review)

Field	Description	Example
review_id, review_url, date, datetime, language	Review ID, link, date and review language	eng
title, pros, cons, advice_to_management	The text the reviewer wrote	text
overall_rating and six rating_ fields	Overall rating and sub-ratings, out of 5	4.0
recommend_to_friend, ceo_approval, business_outlook	Answers such as positive or approve	positive
job_title, employment_status, is_current_employee, years_employed, location, location_type	Reviewer role and work location (no name)	Recruiter
helpful_count, not_helpful_count, featured, employer_responses	Votes and employer replies	0

Interviews (type = interview)

Field	Description	Example
interview_id, interview_url, date, interview_date	ID, link and dates	2025-05-30
job_title, location, outcome, experience, difficulty, how_obtained, duration_days	Role, place and result of the interview process	no_offer
process, questions, advice, negotiation	Interview description, questions asked and advice	text

Salaries (type = salary)

Field	Description	Example
job_title, job_title_id, country, currency, pay_period	Job title and the country, currency and period the pay is for	annual
salary_count, most_recent_submission	Number of reports and date of the latest one	42
base_pay, total_pay, additional_pay_mean	Pay figures; total pay holds percentiles	object

Jobs (type = job)

Field	Description	Example
job_id, title, normalized_title, company_id, company_name, company_rating	Listing, title and hiring company	Data Analyst
location, posted_days_ago, posted_date, easy_apply, sponsored, expired	Place, age and listing status	New York, NY
salary_min, salary_median, salary_max, salary_currency, pay_period, salary_source	Pay estimate	USD
skills, job_attributes, description_snippet, job_url, apply_url	Skills, attributes and links	list
description, company_details, coordinates	Filled only when Full job descriptions is on	text
search_keyword, search_location	The search that found the job	data analyst

These job rows are glassdoor job listings data from company pages and the keyword search.

Problems (type = error): input, company_id, dataset and error explain which input or list could not be processed.

Review rows give you a glassdoor company reviews dataset for text analysis.

Interview rows form a glassdoor interview questions dataset.

Salary rows form a glassdoor salary dataset. Group them by job_title to compare employer salaries.

For employee review analysis, start with pros, cons, title and overall_rating. Export those fields as a sentiment analysis dataset.

💡 Use cases for Glassdoor data

• Check a company before applying: read rating_overall, the sub-ratings and the pros and cons of recent reviews before you send an application or accept an offer.
• Employee sentiment analysis: combine pros, cons, overall_rating and date from many reviews, or use the counts in review_insights as a starting point.
• Interview preparation: read questions, process, difficulty and outcome of past interviews at the company you apply to.
• Salary benchmarking: compare base_pay and total_pay percentiles per job_title and currency.
• Competitor hiring analysis: collect job rows for several companies and compare title, location and posted_date.
• Employer reputation tracking: schedule the Actor with date_from and compare overall_rating by date between runs.
• Workplace culture comparison: put rating_culture_values, rating_work_life_balance and rating_senior_management of two employers side by side.
• Company due diligence: combine ceo_approval_pct, recommend_to_friend_pct and review trends in one table.

The same rows help HR teams and analysts with employer research, and recruiters with market data. Plot overall_rating by date to see change over time.

🛠️ How to scrape Glassdoor reviews

Follow these steps to scrape glassdoor reviews for one company or many.

1. Create a free Apify account and open Glassdoor Reviews & Jobs Scraper in Apify Console.
2. Add company names, Glassdoor links or employer IDs to Companies, links or IDs.
3. Choose Data to collect: reviews, interviews and jobs. The company profile and salaries are always included.
4. Set Max reviews per company (0 collects every review), and optionally Only newer than to keep recent rows.
5. Click Start and watch the log and the Output tab fill.
6. Download glassdoor reviews as JSON, CSV, Excel, XML or HTML.

💡 Start with a small Max reviews per company, such as 5, to see the result before a large run.

How to scrape Glassdoor interview questions

To scrape glassdoor interview questions, choose interviews in Data to collect and add the company name. This works as a glassdoor interview scraper for one company or a list. Read the interview questions by company in the questions field.

How to scrape Glassdoor salaries

To scrape glassdoor salaries, add the companies. Salary rows are always collected, so the Actor also works as a glassdoor salary scraper for any list of employers.

How to scrape Glassdoor company ratings

To scrape glassdoor company ratings, read the company row: rating_overall and the six sub-ratings. Read it first for company research before a job interview.

💰 How much does it cost to scrape Glassdoor?

Glassdoor Reviews & Jobs Scraper uses pay per event pricing. Every row saved to the main table is one main_result event: a company profile, a review, an interview, a salary row or a job. A run also pays one apify-actor-start event of $0.00005 for its 1,024 MB of memory. Apify platform usage (compute) is billed on top of the events above.

Apify plan tier	Price per 1,000 results	Price per result
Free	$5.00	$0.005
Bronze	$4.50	$0.0045
Silver	$4.00	$0.004
Gold, Platinum, Diamond	$3.50	$0.0035

Example: 1,000 results on the Free tier cost 1,000 x $0.005 = $5.00, plus $0.00005 for the start, which is $5.00005 before platform usage. The same 1,000 results on the Gold tier cost 1,000 x $0.0035 = $3.50, plus $0.00005, which is $3.50005.

⚠️ Salary rows are always collected, one per job title, and a large company can have hundreds. If a company has 600 job titles with salary data, they add 600 results, which is 600 x $0.005 = $3.00 on the Free tier. Error rows and the section tables are not billed as results.

You can set a spend limit on a run, and the Actor stops cleanly when it is reached. See the Pricing tab of the Actor for the current numbers.

⬇️ Input

All fields are optional, but a run needs at least one company or a job keyword or location. The form is prefilled with two example companies and small limits.

Field	Meaning	Default
urls	Company names, Glassdoor company links from any tab and country, or numeric employer IDs. Leave empty only for the keyword job search.	empty
get	Lists to collect for every company: reviews, interviews, jobs.	["reviews"]
max_reviews, max_interviews, max_jobs	Rows per company; 0 collects all. Reviews and interviews come newest first. Jobs use the same limit for each company and for the keyword search.	0
date_from	Keep only rows on or after a date such as 2025-01-01, or a relative value such as 90d, 6m or 2y.	empty
job_descriptions	Add the full description, employer block and map position to each job (one extra request per job).	false
proxyConfiguration	Optional proxy. No proxy is used by default.	no proxy

Reviews are fetched language by language: English, French, German, Spanish, Portuguese, Italian and Dutch. They are newest first inside each language, not across all languages. With a Max reviews per company limit, the limit fills from English first.

Keyword job search. Besides company jobs, the Actor can search jobs across all employers. Fill job_keyword, job_location or both, and the search runs once. These filters then apply: job_type (any, fulltime, parttime, contract), job_remote_only, job_easy_apply_only, job_seniority (all, entrylevel, midseniorlevel, director, executive, internship), job_min_company_rating (any or 1 to 5), job_min_salary and job_max_salary (yearly, 0 means no limit), job_radius_miles, job_posted_within_days and job_sort (relevance or date). This search is the basis for job market analysis.

{
  "urls": ["Example Corp"],
  "get": ["reviews"],
  "max_reviews": 2,
  "job_descriptions": false,
  "proxyConfiguration": { "useApifyProxy": false }
}

Replace Example Corp with a real company name or link.

⬆️ Output

The scraped rows appear as a dataset in the Storage tab. The table view does not show all fields, so export the dataset or open it through the API for the full rows. To export glassdoor reviews to excel, open the dataset in the Storage tab and choose Excel. The Output tab adds tables for companies, reviews, interviews, salaries, jobs and problems.

The sample below is an abbreviated illustration with made-up names, text and numbers that matches the input above. Your run returns real Glassdoor data. The company profile row is saved after its lists.

[
  {
    "type": "review",
    "company_id": 1000001,
    "company_name": "Example Corp",
    "review_id": 900000001,
    "date": "2025-06-12",
    "language": "eng",
    "title": "Good team, slow promotions",
    "overall_rating": 4.0,
    "pros": "Flexible hours and a helpful team",
    "cons": "Promotions take a long time",
    "job_title": "Analyst",
    "is_current_employee": true,
    "location": "Example City",
    "rating_work_life_balance": 5.0,
    "recommend_to_friend": "positive",
    "ceo_approval": "approve"
  },
  {
    "type": "salary",
    "company_id": 1000001,
    "company_name": "Example Corp",
    "job_title": "Analyst",
    "country": "United States",
    "currency": "USD",
    "pay_period": "annual",
    "salary_count": 42,
    "total_pay": { "p10": 60000, "median": 75000, "p90": 95000 }
  },
  {
    "type": "company",
    "company_id": 1000001,
    "company_name": "Example Corp",
    "rating_overall": 3.8,
    "review_count": 1620,
    "reviews_fetched_by_language": { "eng": 2 },
    "review_insights": { "reviews_analyzed": 2, "average_rating": 4.0 }
  }
]

A run also saves a RUN_SUMMARY record in the key-value store with companies, rows_saved, rows_charged, rows_by_type, problems, duration_seconds and spend_limit_reached.

⚠️ Limitations and error handling

• The Actor returns public glassdoor employer data only. It does not read private pages, accounts or anything behind a login.
• A field that Glassdoor does not show stays null. advice and negotiation in interviews are filled only when Glassdoor shows them. Benefits are not collected.
• Salary rows state their country, and the figures are for that one country.
• If Glassdoor refuses requests, the Actor waits, switches to another connection and retries. When that keeps failing, the run stops and keeps every row already saved.
• A very large list takes a long time. Use date_from or a limit to keep a run short.
• The review_insights field is calculated only from the reviews your run collected, so a low max_reviews gives small counts.
• The Actor keeps no state between runs and does not remove rows you already have.

An input that cannot be processed becomes an error row in the main table and the errors table. It is not billed.

{
  "type": "error",
  "input": "https://www.glassdoor.com/Example",
  "error": "no employer id (E<digits>) found in URL"
}

Message or status	What it means	What to do
Done: N rows saved, M problems.	The run finished.	Open the Output tab.
Spend limit reached after N rows.	Your spend limit was reached and the run stopped cleanly.	Raise the maximum charge per run and run again.
No rows collected - see the errors table or the log.	Nothing was saved.	Check the company names or links, then run again.
Input problem: ...	The input could not be read, for example no company and no job keyword.	Add a company, link or ID, or a job keyword or location.
no employer id (E<digits>) found in URL	A link has no Glassdoor employer ID.	Use a company link from the reviews, interviews, salaries or overview tab.

🔌 Integrating Glassdoor Reviews & Jobs Scraper with other apps

Run Glassdoor Reviews & Jobs Scraper from the Apify API and send the dataset to your tools with Apify integrations. Use Make, Zapier or webhooks to pass a finished run to a spreadsheet, a database or a chat message. Keep the exported tables next to your own hr analytics data.

curl -X POST "https://api.apify.com/v2/actors/data_wiki~glassdoor-reviews-jobs-scraper/runs" \
  -H "Authorization: Bearer <YOUR_API_TOKEN>" -H "Content-Type: application/json" \
  -d '{"urls": ["Example Corp"], "get": ["reviews"], "max_reviews": 50}'

See the Apify API reference for the other calls. Create a schedule, set date_from to a relative value such as 30d, and compare the datasets of different runs for employee sentiment tracking. Compare ceo_approval_pct between runs when you do ceo approval rating tracking. AI agents can find and run this Actor through the Apify MCP server.

🔗 Other Glassdoor and review scrapers by data_wiki

The first Actor below reads the same website. The other three come from the same developer and cover related data, so use them only if you also need that kind of data.

Actor	What it extracts	When to use it
Glassdoor Jobs Scraper	Job listings from keywords, search links, job links or job IDs, with pay and company details, and an optional market salary benchmark	You start from a job keyword and a location. Its reviews table holds review highlights and benefit mentions, not full reviews
Contact Details Scraper	Emails, phone numbers and social links from a list of websites	You want contact details for the website of a company you researched here
BuiltWith Official Technology Scraper	The technologies a website is built with	You look at a company website next to its employer data; the link is weak
Tripadvisor Review Scraper	Reviews of hotels, restaurants and attractions on Tripadvisor	You need review text from travel places, not from employers
Profile details for LinkedIn lists	Public LinkedIn profile records with career insights and employer details	You researched an employer and want details on specific people there.
Travel place reviews from Tripadvisor	Reviews of hotels, restaurants, attractions and tours	When you also compare how customers rate travel places

For a keyword and location search on Glassdoor, the keyword and location job search on Glassdoor Actor is built for that. Emails and phone numbers from company websites can follow once you have the website field. A website technology lookup and hotel and restaurant reviews cover different subjects.

⚖️ Is it legal to scrape Glassdoor?

Glassdoor Reviews & Jobs Scraper only reads public Glassdoor pages that any visitor can open. It needs no login and does not read private data or accounts. You are responsible for following Glassdoor's terms and the laws that apply to you.

Reviews and interviews are written by employees. The rows hold the text, job title, location and years employed, but no reviewer name or account. The text can still identify a person, so collect it only with a legitimate reason, follow rules such as GDPR and CCPA, and never use the data to identify or contact reviewers. Read Apify's guides on the legality of web scraping and ethical web scraping, and ask a lawyer if you are unsure.

❓ FAQ

What does the Glassdoor reviews scraper collect?

It collects a company profile, reviews, interviews, salaries and jobs for each company you add. The profile and salaries are always included; reviews, interviews and jobs depend on Data to collect.

Do I need a Glassdoor login or API key?

No. As a Glassdoor scraper, the Actor reads public pages and needs no Glassdoor account. It also needs no glassdoor api key. You only need an Apify account to run it.

Is there a Glassdoor reviews API?

You run this Actor through the Apify API, which also gives you the dataset. It is not an official Glassdoor API. If you looked for a glassdoor api alternative, this Actor returns reviews, interviews, salaries and jobs as structured rows.

How many reviews can I get?

Leave Max reviews per company at 0 for every review the run can reach, because the Actor sets no review limit of its own. Reviews come newest first inside each language. Set a number when a script needs a fixed amount.

How much does it cost?

Each saved row costs $0.005 on the Free tier, down to $0.0035 on the Gold tier and above, plus a start event of $0.00005. See the cost section above for the table and the note on salary rows.

Which Glassdoor Actor should I use?

Use this Actor when you start from companies and want reviews, interviews and salaries, with optional jobs. Use Glassdoor Jobs Scraper when you start from a job keyword and a location and want job listings with an optional market salary benchmark. Both read public Glassdoor pages without a login.

Can I get only salaries?

Not alone. Salary rows are always collected with the company profile, which gives you glassdoor salary data for every company in the list. They also work as salary data by job title. Choose interviews in get and set max_interviews to 1 to keep the other lists small.

Why are some fields empty?

A field stays null when Glassdoor does not show it for that row. Fields such as stock_symbol can be empty for most companies.

Do I need proxies?

No. No proxy is used by default. If Glassdoor refuses requests, the Actor switches to another connection on its own, and you can set your own proxy in the input.

Can I use Glassdoor Reviews & Jobs Scraper through an MCP server?

Yes. AI agents can find and run Apify Actors through the Apify MCP server, then read the dataset of the run.

💬 Your feedback

Found a bug or missing data? Open an issue from the Actor page in Apify Console and include the run link. Need a custom solution? Contact hello.datawiki@gmail.com.