Google Jobs Scraper

Search Google Jobs by keyword and extract structured job listings — titles, companies, locations, salaries, employment types, source domains, and posting dates by default. Optional detail fetching adds descriptions, apply URLs, and qualification highlights when you enable it. Supports location filtering, country, and language settings.

What does Google Jobs Scraper do?

Google Jobs Scraper searches Google's job aggregation engine and extracts detailed, structured data from every listing. Google Jobs pulls listings from thousands of job boards (Indeed, LinkedIn, Glassdoor, ZipRecruiter, company career pages) into one unified interface — this scraper turns those results into clean, exportable data.

Enter your job search keywords, and the scraper extracts the job cards Google shows in results. By default it returns fast SERP-card data: title, company, location, salary, employment type, posting date, source domain, query, and scrape timestamp. Enable Fetch full job details when you also need descriptions, qualifications, responsibilities, benefits, and direct apply URLs.

The scraper primarily uses an HTTP Google SERP path for efficient extraction, with a bounded browser fallback available for difficult queries. Detail fetching is opt-in because opening every job detail page is slower and more expensive.

Who is Google Jobs Scraper for? (Who is it for)

• 🏢 Recruiters and HR teams tracking job market trends and competitor postings across industries
• 📊 Compensation analysts collecting salary data across roles, locations, and companies
• 🔍 Job aggregator platforms building comprehensive job databases from Google's curated listings
• 🧪 Labor market researchers studying employment trends, skill demand, and hiring patterns
• 🤖 Job board startups sourcing listings from Google's aggregated feed of thousands of boards
• 💼 Career coaches analyzing what employers look for in specific roles and industries
• 📈 Data analysts building datasets for workforce analytics, salary benchmarking, and market intelligence
• 🏛️ Policy researchers studying employment patterns, remote work trends, and regional job availability

Why use Google Jobs Scraper?

• 🌐 Aggregated from thousands of sources — Google Jobs combines listings from Indeed, LinkedIn, Glassdoor, ZipRecruiter, and company career pages in one place
• 📋 Fast default job cards — title, company, location, salary, employment type, source domain, and posting date
• 🔎 Optional full details — enable detail fetching for descriptions, direct apply URLs, qualifications, responsibilities, and benefits
• 🌍 Location + language filtering — target specific countries, cities, and languages for localized results
• 📦 Multiple queries per run — search for "software engineer", "data analyst", and "product manager" in a single run
• 💰 Pay per result — only pay for jobs actually extracted, no flat subscription fees
• ⚡ Batch processing — extract up to 200 jobs per query across multiple search terms

How much does it cost to scrape Google Jobs?

Google Jobs Scraper uses pay-per-event pricing. You only pay for what you use:

Event	Price
Run started (one-time)	$0.035
Per job scraped (Free tier)	$0.006
Per job scraped (Bronze)	$0.0054
Per job scraped (Silver)	$0.0048
Per job scraped (Gold)	$0.0039
Per job scraped (Platinum)	$0.003
Per job scraped (Diamond)	$0.0024

Example costs (Free tier):

• 10 jobs for 1 keyword: $0.035 + (10 x $0.006) = $0.095
• 50 jobs for 2 keywords: $0.035 + (100 x $0.006) = $0.635
• 200 jobs for 1 keyword: $0.035 + (200 x $0.006) = $1.235

Apify Free plan users get $5/month in free credits — enough for ~833 job listings.

Data you can extract from Google Jobs

Field	Type	Description
title	string	Job title (e.g. "Senior Software Engineer")
company	string	Employer name
location	string	Job location (city, state, or "Remote")
description	string	Full job description text. Blank unless includeDetails is enabled.
salary	string	Salary range if available (e.g. "$120K–$160K a year")
employmentType	string	Full-time, Part-time, Contract, Internship
datePosted	string	When the job was posted (e.g. "3 days ago")
sourceUrl	string	URL of the original job posting
sourceDomain	string	Domain of the source site (e.g. "linkedin.com")
applyUrl	string	Direct link to apply for the job. Blank unless includeDetails is enabled.
highlights.qualifications	string[]	Required qualifications and skills. Empty unless includeDetails is enabled.
highlights.responsibilities	string[]	Key job responsibilities. Empty unless includeDetails is enabled.
highlights.benefits	string[]	Listed benefits and perks. Empty unless includeDetails is enabled.
query	string	The search keyword that returned this result
scrapedAt	string	ISO 8601 timestamp when the data was collected

How to scrape Google Jobs step by step

1. Go to Google Jobs Scraper on Apify Store
2. Click Try for free
3. Enter your job search keywords (e.g., "software engineer", "data analyst")
4. Optionally add a location filter (e.g., "New York", "Remote", "London")
5. Set the maximum number of jobs per query (default: 20)
6. Enable Fetch full job details only if you need descriptions, apply URLs, and highlight sections
7. Adjust country and language if needed (default: US, English)
8. Click Start and wait for results
9. Download your data as JSON, CSV, Excel, or connect it to your workflow

Input configuration

Field	Type	Description	Default
queries	string[]	Job search keywords (e.g., "software engineer", "nurse"); up to 10 queries per run	required
location	string	Location filter appended to each query (e.g., "New York", "Remote")	none
maxResults	integer	Maximum jobs to extract per query (1–200)	20
includeDetails	boolean	Open each job detail page to populate description, applyUrl, and highlights; disabled by default for faster, lower-cost runs	false
country	string	Country code for localized results (e.g., "us", "uk", "de")	"us"
language	string	Language code for results (e.g., "en", "de", "fr")	"en"
maxRequestRetries	integer	Optional browser fallback attempts if the primary HTTP path returns no jobs (0–2)	0

Example input

{
    "queries": ["software engineer", "data analyst"],
    "location": "New York",
    "maxResults": 20,
    "includeDetails": false,
    "country": "us",
    "language": "en"
}

Output example

{
    "title": "Senior Software Engineer",
    "company": "Google",
    "location": "New York, NY",
    "description": "We're looking for a Senior Software Engineer to join our Cloud Platform team. You will design and build large-scale distributed systems, mentor junior engineers, and drive technical strategy...",
    "salary": "$160,000–$210,000 a year",
    "employmentType": "Full-time",
    "datePosted": "3 days ago",
    "sourceUrl": "https://careers.google.com/jobs/results/1234567890",
    "sourceDomain": "careers.google.com",
    "applyUrl": "https://careers.google.com/jobs/results/1234567890/apply",
    "highlights": {
        "qualifications": [
            "Bachelor's degree in Computer Science or equivalent",
            "5+ years of experience in software development",
            "Proficiency in Python, Java, or Go",
            "Experience with distributed systems and cloud infrastructure"
        ],
        "responsibilities": [
            "Design and implement scalable backend services",
            "Mentor junior team members and conduct code reviews",
            "Collaborate with product teams to define technical requirements",
            "Drive architectural decisions for new features"
        ],
        "benefits": [
            "Competitive salary and equity",
            "Health, dental, and vision insurance",
            "Flexible remote work policy",
            "Annual education stipend"
        ]
    },
    "query": "software engineer",
    "scrapedAt": "2026-03-27T14:30:00.000Z"
}

Tips for best results

• 🎯 Use specific job titles — "frontend react developer" returns more relevant results than just "developer"
• 📍 Add location for local jobs — use the location field for city-specific searches like "Austin, TX" or "London"
• 🌐 Try "Remote" as location — filter for remote-only positions by setting location to "Remote"
• 📏 Start small — test with 10 results first, then scale up once you verify the output matches your needs
• 🔄 Batch related queries — add up to 10 job titles in one run to build a comprehensive dataset efficiently
• 💡 Use country codes — set country to "uk" for British job listings or "de" for German ones
• 🗣️ Match language to country — set language to "de" when searching German jobs for localized titles and descriptions
• 📊 Check salary fields — not all listings include salary data; Google shows it when the source provides it

Integrations

Connect Google Jobs data to your existing tools and workflows:

• 📊 Google Sheets — automatically export job listings to a spreadsheet for tracking and analysis
• 🔗 Zapier / Make — trigger workflows when new jobs matching your criteria appear
• 💾 Webhooks — push results to your own API endpoint in real time
• 📁 S3 / Google Cloud Storage — store large job datasets for batch processing
• 📈 Power BI / Tableau — import structured job data for salary benchmarking dashboards
• 🤖 Slack notifications — get alerts when new jobs matching your keywords are posted
• 📧 Email alerts — receive daily digests of new job listings via Apify integrations

Using Google Jobs Scraper with the API (API Usage)

Node.js

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: 'YOUR_API_TOKEN' });

const run = await client.actor('automation-lab/google-jobs-scraper').call({
    queries: ['software engineer', 'data analyst'],
    location: 'New York',
    maxResults: 50,
    country: 'us',
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(`Found ${items.length} job listings`);
items.forEach((job) => {
    console.log(`${job.title} at ${job.company} — ${job.salary || 'Salary not listed'}`);
});

Python

from apify_client import ApifyClient

client = ApifyClient('YOUR_API_TOKEN')

run = client.actor('automation-lab/google-jobs-scraper').call(run_input={
    'queries': ['software engineer', 'data analyst'],
    'location': 'New York',
    'maxResults': 50,
    'country': 'us',
})

items = client.dataset(run['defaultDatasetId']).list_items().items
print(f'Found {len(items)} job listings')
for job in items:
    print(f"{job['title']} at {job['company']} — {job.get('salary', 'Salary not listed')}")

cURL

curl "https://api.apify.com/v2/acts/automation-lab~google-jobs-scraper/runs" \
  -X POST \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_TOKEN" \
  -d '{
    "queries": ["software engineer"],
    "location": "New York",
    "maxResults": 20,
    "country": "us"
  }'

Using with MCP (Model Context Protocol)

Claude Code

Add Google Jobs Scraper to your Claude Code setup:

$claude mcp add --transport http apify "https://mcp.apify.com?tools=automation-lab/google-jobs-scraper"

Then ask Claude: "Search Google Jobs for 'product manager' in San Francisco and show me the top 20 listings with salaries"

Claude Desktop

Add this to your Claude Desktop claude_desktop_config.json:

{
    "mcpServers": {
        "apify": {
            "url": "https://mcp.apify.com?tools=automation-lab/google-jobs-scraper"
        }
    }
}

Then ask:

• "Find remote data engineering jobs and list them with salary ranges"
• "What are companies hiring for 'machine learning engineer' in New York?"
• "Search for entry-level marketing jobs in London and show me the qualifications required"

Is it legal to scrape Google Jobs? (Legality)

Google Jobs Scraper accesses only publicly available content — the same job listings any visitor sees when searching on Google. Web scraping of public data is generally considered legal, as established by the U.S. Ninth Circuit's ruling in hiQ Labs v. LinkedIn (2022).

This scraper does not:

• Access private or restricted content
• Bypass authentication or paywalls
• Collect personal data beyond what is publicly displayed
• Circumvent any technical protection measures

The data extracted consists of job postings that employers have intentionally made public. Always review and comply with applicable laws in your jurisdiction before scraping.

FAQ

How many jobs can I extract per query? Up to 200 jobs per query. Google Jobs loads results progressively as you scroll, so larger requests take proportionally longer. For most use cases, 20–50 jobs per query provides a good balance of coverage and speed.

Why am I getting fewer results than the max I set? Google may have fewer matching jobs for your query and location combination. The scraper extracts everything Google shows — if Google only has 15 matching jobs, that's all you'll get.

Why is the salary field empty for some jobs? Google Jobs only displays salary information when the source site provides it. Many employers don't include salary ranges in their listings. This is a limitation of the source data, not the scraper.

Does it support non-English job searches? Yes. Set the country and language fields to match your target market. For example, use country: "de" and language: "de" for German job listings.

The scraper returned 0 results — what happened? This usually means Google showed a CAPTCHA or verification page. The scraper automatically retries with a fresh session. If it persists, try running again — Google's bot detection is session-dependent and residential proxies typically resolve this.

Can I filter by employment type (full-time, part-time, remote)? Include these terms in your search query, e.g., "software engineer remote" or "part-time data entry". Google Jobs interprets these naturally, just like searching on Google directly.

Can I schedule recurring job searches? Yes. Use Apify's built-in scheduling to run searches hourly, daily, or weekly. This is ideal for monitoring job markets, tracking new postings, or building time-series datasets of job availability.

What job boards does Google Jobs pull from? Google aggregates listings from thousands of sources including Indeed, LinkedIn, Glassdoor, ZipRecruiter, Monster, company career pages, staffing agencies, and government job sites. You get a unified view without scraping each board individually.

Related scrapers

• Indeed Scraper — scrape job listings directly from Indeed
• LinkedIn Jobs Scraper — extract job postings from LinkedIn Jobs
• Glassdoor Jobs Scraper — scrape jobs and company data from Glassdoor
• Greenhouse Jobs Scraper — extract job listings from Greenhouse-powered career pages
• Naukri Scraper — scrape job listings from Naukri.com (India)
• Google Search Scraper — scrape general Google search results