# Glassdoor Scraper - Jobs, Companies & Salaries (`makework36/glassdoor-scraper`) Actor

Scrape Glassdoor job listings: title, company, location, salary parsed, employment type, description. 25+ structured fields per job via JSON-LD. HTTP-only, low cost.

- **URL**: https://apify.com/makework36/glassdoor-scraper.md
- **Developed by:** [deusex machine](https://apify.com/makework36) (community)
- **Categories:** Jobs, Lead generation
- **Stats:** 3 total users, 0 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

Pay per event

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For usage examples, see the [API](#api) section below.

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).

# README

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

A high-performance **Glassdoor scraper** that extracts complete job listings from [Glassdoor.com](https://www.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](https://www.linkedin.com/jobs), [Indeed](https://www.indeed.com), and [BLS](https://www.bls.gov) data, salary benchmarking firms, founders monitoring competitor hiring patterns, and ML teams training resume-to-job matching pipelines against the [schema.org JobPosting](https://schema.org/JobPosting) schema. Output is clean JSON, ready for [Google BigQuery](https://cloud.google.com/bigquery), Snowflake, Postgres, or [Google Sheets](https://workspace.google.com/products/sheets/).

### ✨ Why use this scraper

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](https://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.

### 📤 Output fields

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

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

#### Mode 1 — Search by keywords (easiest)

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

#### Mode 2 — Multi-country pipeline

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

#### Mode 3 — Pre-built search URLs (advanced)

```json
{
  "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

| 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

```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"
}
```

### 💻 Code examples

#### From the Apify API (curl)

```bash
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)

```python
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)

```javascript
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

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

### ⚡ Performance

| 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.

### 📊 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 |

### 💵 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 actors

- [`makework36/indeed-scraper`](https://apify.com/makework36/indeed-scraper) — Indeed.com US jobs
- [`makework36/naukri-scraper`](https://apify.com/makework36/naukri-scraper) — Naukri.com India jobs
- [`makework36/trustpilot-reviews-scraper`](https://apify.com/makework36/trustpilot-reviews-scraper) — Trustpilot reviews
- [`makework36/google-maps-scraper-full`](https://apify.com/makework36/google-maps-scraper-full) — Google Maps places

# Actor input Schema

## `mode` (type: `string`):

'keywords' searches Glassdoor by keyword. 'urls' takes pre-built search URLs.

## `keywords` (type: `array`):

Job titles or keywords to search (e.g. 'python developer', 'data scientist').

## `searchUrls` (type: `array`):

Pre-built Glassdoor search URLs.

## `country` (type: `string`):

Glassdoor regional domain to search on.

## `maxJobs` (type: `integer`):

Maximum jobs per keyword/URL. Glassdoor shows ~30 jobs per page.

## `fetchDetails` (type: `boolean`):

Visit each job detail page for full description + JSON-LD JobPosting (salary, employment type, address). Slower but ~20 extra fields.

## `proxyConfiguration` (type: `object`):

Apify proxy. Residential recommended to avoid Glassdoor rate-limiting.

## Actor input object example

```json
{
  "mode": "keywords",
  "keywords": [
    "python developer"
  ],
  "searchUrls": [],
  "country": "us",
  "maxJobs": 10,
  "fetchDetails": true,
  "proxyConfiguration": {
    "useApifyProxy": true,
    "apifyProxyGroups": [
      "RESIDENTIAL"
    ]
  }
}
```

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "proxyConfiguration": {
        "useApifyProxy": true,
        "apifyProxyGroups": [
            "RESIDENTIAL"
        ]
    }
};

// Run the Actor and wait for it to finish
const run = await client.actor("makework36/glassdoor-scraper").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = { "proxyConfiguration": {
        "useApifyProxy": True,
        "apifyProxyGroups": ["RESIDENTIAL"],
    } }

# Run the Actor and wait for it to finish
run = client.actor("makework36/glassdoor-scraper").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "proxyConfiguration": {
    "useApifyProxy": true,
    "apifyProxyGroups": [
      "RESIDENTIAL"
    ]
  }
}' |
apify call makework36/glassdoor-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,makework36/glassdoor-scraper"
        }
    }
}

```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/vsdDzefwTJMp8015k/builds/wznKcPMqNRMCirOyE/openapi.json