In this guide, we'll show you how to scrape Google Jobs listing results using our Google Search API. We'll build a simple Python script, render the jobs panel, and extract structured job data step by step. By the end, you'll be able to collect job titles, companies, locations, and posting dates programmatically. Many of our Google Jobs Scraper users struggle with rendering the jobs panel correctly, constructing valid search queries, and parsing the job listings that appear dynamically on the page. We'll cover each of these in simple steps so you can build a working scraper without running into those common issues.
Quick Answer (TL;DR)
To scrape Google Jobs listing results with our web scraping API:
- Build a Google search URL for the job title and location you want to target.
- Send a GET request from Python to load the Google Jobs results page.
- Enable JavaScript rendering so the jobs panel and dynamic listings appear.
- Parse the response HTML and extract the job cards and their key fields.
- Save the structured output to JSON or CSV for later use.
Quickstart: Full Parser Code
If you want to skip the tutorial and start scraping immediately with our API, here you can copy our entire script to start targeting multiple job boards and save them in a CSV file. Just adjust the position and location parameters within the asyncio loop and you are good to go!
import asyncio
from scrapingbee import ScrapingBeeClient
import pandas as pd
import json
client = ScrapingBeeClient(api_key='YOUR_API_KEY')
def google_jobs_api(position='jobs', location="USA"):
extract_rules = {
"jobs": {
"selector": ".EimVGf",
"type": "list",
"output": {
"title": "div.tNxQIb.PUpOsf",
"company": "div.wHYlTd.MKCbgd.a3jPc",
"location_and_portal": "div.wHYlTd.FqK3wc.MKCbgd",
"posted": "span[aria-label$=ago]",
"employment_type": "span[aria-label^=Employment]",
"salary": "span[aria-label^=Salary]",
"job_detail": ".MQUd2b@href"
}
}
}
js_scenario = {
"instructions":[
{"evaluate":"(async()=>{let startTime=Date.now();let timeLimit=30000;let lastScrollHeight=0;function sleep(ms){return new Promise(resolve=>setTimeout(resolve,ms));}async function scrollToEnd(){while(Date.now()-startTime<timeLimit){window.scrollTo(0,document.body.scrollHeight);await sleep(1000);let currentScrollHeight=document.body.scrollHeight;if(currentScrollHeight===lastScrollHeight){console.log('Reached the end of the page.');break;}else{lastScrollHeight=currentScrollHeight;}}console.log('Scrolling completed or time limit reached.');}await scrollToEnd();let indicator=document.createElement('div');indicator.id='scrollCompleted';document.body.appendChild(indicator);})();"},
{"wait_for": "#scrollCompleted"}
]
}
position = position.replace(' ', '+')
location = location.replace(' ', '+')
response = client.get(
f'https://www.google.com/search?q={position.replace(" ","+")}+job+near+{location.replace(" ","+")}&udm=8',
params={
"custom_google": "true",
"stealth_proxy": "true",
"render_js": "true",
"extract_rules": extract_rules,
"js_scenario": js_scenario,
"country_code": "us"
},
retries=2
)
job_data = response.json().get('jobs', [])
df = pd.DataFrame(job_data)
df.to_csv("google_jobs_output.csv", mode='a', index=False, header=False)
return {'count': len(job_data)}
async def run_all_queries():
loop = asyncio.get_event_loop()
tasks = [
loop.run_in_executor(None, google_jobs_api, "Software Developer", "New York, USA"),
loop.run_in_executor(None, google_jobs_api, "Data Analyst", "San Francisco, USA"),
loop.run_in_executor(None, google_jobs_api, "Marketing Manager", "Austin, USA"),
]
results = await asyncio.gather(*tasks)
asyncio.run(run_all_queries())
Setting Up Your Python Environment
The ScrapingBee HTML API is a great asset that greatly simplifies data extraction from resilient sources. All you need is a functional Python environment that integrates our tools and essential libraries to infuse the script with its intended functionality.
Python is the most popular coding language and an undeniable favorite for automated data collection. Its ease of use and seamless integration of external libraries is enough to empower even the absolute beginners.
Note: For more information on coding essentials, check out our post on Python web scraping.
Install Python and required libraries
Before we begin, make sure to install Python 3.6 or later — on Windows, download the installer from python.org, run it, and tick “Add Python to PATH”;
Note: On macOS, use the official .pkg or Homebrew (brew install python); on Linux, install via your distro’s package manager (apt, dnf, etc.)
For this tutorial, we will focus on the Windows setup. Proceed with the instructions in the installation wizard. Once completed, verify the version by running this line of code in your Command Prompt:
python --version
If installation is successful, you will see your Python version. Once everything is in check, use Python Installs Packages (PIP) to install the necessary packages from the Python Package Index (PyPI):
scrapingbee – routes your requests through a headless-browser API, handling JavaScript and proxies so you get clean HTML.
pandas – structures the scraped data into DataFrames, making filtering, cleaning, and export painless.
beautifulsoup4 – parses the raw HTML, letting you locate and extract tags, text, and attributes with simple selectors.
You can install each package individually, or separate each one by a space to handle the installation in one line, like this:
pip install scrapingbee pandas beautifulsoup4
These libraries will handle HTTP requests and data manipulation, respectively. Our API will extract the data, and beautifulsoup4 and pandas work together to clean up, organize, and structure data in a readable and understandable format.
Note: Want to work with different coding tools? Check out our guide on Best Python scraping libraries!
After successful installation, you can double-check the packages with a pip list command.
Get access to a scraping API
If its your first time working with ScrapingBee, Sign up and test the benefits yourself with our free trial of 1000 free API calls! Experience the convenience of our tools firsthand before further commitments to see for yourself the broad scope of unlocked scraping capabilities!
Register your account to get your API key. After registration, you'll find your key in the dashboard. This key is essential for authenticating your requests to our API.
After signing up, you'll see a dashboard showing your free trial expiration, available credits, concurrent connections, and — most importantly — your API key. Copy it so we can start building our scraper.
Test your first API request
To make the first API request, it's time to prepare the coding environment and import the downloaded scraping libraries into the script. Start your code by entering these lines:
import pandas
from scrapingbee import ScrapingBeeClient
from bs4 import BeautifulSoup
Note: Python library imports have to be at the top of your coding script before invoking their tools.
Now it's time to create the connection variable client (or any name of your choice) which will calls the creation of the ScrapingBee client. Enter the API key in the parentheses:
# Set up the ScrapingBee client
client = ScrapingBeeClient(api_key='YOUR_API_KEY')
Once that is set up, create the response variable, which will store the data from the GET API call, following your provided wepbage URL:
# Store data in the "response" variable by invoking the GET method in your ScrapingBee client
response = client.get("YOUR-URL")
The GET API call in the response element can be customized with parameters, defined within the parentheses of your API call. When you plan to extract information from Google, we have two mandatory parameters:
render_js (True/False): Enables/disables the use of a headless browser for JavaScript rendering
custom_google (True/False) : A mandatory parameter for accessing Google and its subdomains with our HTML API.
After the provided webpage URL, add the comma to provide these additional instructions – a dictionary of parameters, stored within the params variable:
# Add ScrapingBee parameters to the GET API call
response = client.get('YOUR URL',
params={
'custom_google': True,
'render_js': True,
}
)
Let's test that everything works by making a simple request to Google. Before extracting any data, let's add the wepbage URL, and a print function that will retrieve the HTML status code.
import pandas
from scrapingbee import ScrapingBeeClient
from bs4 import BeautifulSoup
# Set up the ScrapingBee client
client = ScrapingBeeClient(api_key='YOUR_API_KEY')
# Request rendered Google Jobs search results
response = client.get(
'https://www.google.com/',
params={
'custom_google': True,
'render_js': True,
}
)
# Check if connection is successful, look for response status code: 200
print('Response HTTP Status Code: ', response.status_code)
If your connection is successful, your Command Prompt should look like this:
The render_js=true parameter is crucial here – it tells our API to execute JavaScript, which is necessary for Google Jobs content to appear.
Scrape Data from Google Jobs: Step-by-Step
Now that your environment is ready, let's create a scraper that can handle different job titles and locations. The key to successfully creating these scrapers is constructing valid search URLs that Google understands.
Create search queries and locations
Effective extraction of Google Jobs listings depends on two key factors: dynamic structuring of URLs. By structuring a search query that triggers the Google Jobs widget, you can see the changes in the URL, which will help form dynamic URLs for each scraping request.
Here is an example of an URL that will trigger the widget according to the desired position and location:
https://www.google.com/search?q={position}+job+near+{location}&udm=8
Note: The
&udm=8is a special query parameter that ensures the rendering of the Google Jobs panel instead of standard search results.
For more details on how to approach search results, check out our blog on scraping Google Search!
Pro Tip: The broader our location query, the more jobs we can get from the panel. For example, searching for ‘VLSI Engineer’ in ‘San Antonio’ returns zero results, while changing the location to ‘Texas’ (the state that includes the city of San Antonio) returns some job listings. You can always sort or filter the results by specific locations once we have the data, as the job listings typically mention the city and not just the state.
How Google Jobs Search URLs Work
If you look at the Google URL you’re using to scrape Google jobs listing results, it looks something like:
https://www.google.com/search?q={position}+job+near+{location}&udm=8
This is a URL structure we constructed based on how Google search works. After the /search bit, you will see two parameters. One is q=... and the other is udm=8.
The udm parameter is what triggers the job postings special panel to be shown. The q parameter stands for query, and that is where you supply your search in natural text, for example: “sales rep job near Texas” or “chef job in New York”.
Quite obviously, most of our queries are going to have a position (engineer, chef, etc.) and a location. Since URLs cannot have spaces, the spaces in your query get replaced by ‘+’ as per URL encoding rules. So, if you wanted to scrape Google jobs listings for chef jobs in New York, your URL would look like this:
https://www.google.com/search?q={position}+job+near+{location}&udm=8
Now that you know how to construct the URLs for a given position and location, let’s look at how to scrape Google Jobs using this in Python.
Form dynamic URLs for each query
Next, you'll create properly formatted URLs for Google Jobs searches. To properly structure our data collection tool, you will define a function that will invoke all the elements of our Google Jobs Scraper.
# Creating the google_jobs_api function with position and location parameters
def google_jobs_api(position='jobs', location="USA"):
Within the function, you will redefine the response variable to make sure the GET API call uses a dynamic URL based on these position and location of job listings:
def google_jobs_api(position='jobs', location="USA"):
position = position.replace(' ', '+')
location = location.replace(' ', '+')
response = client.get(
f'https://www.google.com/search?q={position}+job+near+{location}&udm=8',
Prepare the API payload with parsing logic
Now you'll set up your ScrapingBee request with additional parameters:
stealth_proxy: Enables proxy connections that mimic human-like browsing.
country_code: Routes the request through a proxy server in the United States.
response = client.get(
f'https://www.google.com/search?q={position}+job+near+{location}&udm=8',
params={
"render_js": "true",
"custom_google": "true",
"stealth_proxy": "true",
'country_code':'us'
},
retries=2
)
The next portion of contains covers parsing of the extracted HTML code with Beautifulsoup. The soup variable activates the parser and stores the result. After inspecting the Google Jobs search results, you can see that each individual job listing is stored in div elements with the .EimVGf class.
soup = BeautifulSoup(response.text, 'html.parser')
job_cards = soup.select('.EimVGf')
Now the soup variable only stores information from these specific div elements. By inspecting them, you can separate and extract job details and assign them to a desired variable. Let's build a for loop which scans all ements with the .EimVGf class. Every job card is appended into the list of jobs, while each card is defined as a Python dictionary that stores key-value pairs.
jobs = []
for card in job_cards:
title = card.select_one('div.tNxQIb.PUpOsf')
company = card.select_one('div.wHYlTd.MKCbgd.a3jPc')
location = card.select_one('div.wHYlTd.FqK3wc.MKCbgd')
posted = card.select_one('span[aria-label$="ago"]')
employment_type = card.select_one('span[aria-label^="Employment"]')
salary = card.select_one('span[aria-label^="Salary"]')
job_link = card.select_one('.MQUd2b')
jobs.append({
"title": title.get_text(strip=True) if title else None,
"company": company.get_text(strip=True) if company else None,
"location_and_portal": location.get_text(strip=True) if location else None,
"posted": posted.get_text(strip=True) if posted else None,
"employment_type": employment_type.get_text(strip=True) if employment_type else None,
"salary": salary.get_text(strip=True) if salary else None,
"job_detail": job_link['href'] if job_link and job_link.has_attr('href') else None
})
return {
'count': len(jobs),
'jobs': jobs,
'info': f"{response.status_code} {'SUCCESS' if jobs else 'NO JOBS FOUND'}"
}
After defining the GET API request and information parsing steps, you can close the google_jobs_api function definition and invoke it with the desired parameters. Let's test your first script and look for Software Developer listings in New York:
def google_jobs_api(position='jobs', location="USA"):
position = position.replace(' ', '+')
location = location.replace(' ', '+')
response = client.get(
f'https://www.google.com/search?q={position}+job+near+{location}&udm=8',
params={
"custom_google": "true",
"stealth_proxy": "true",
"render_js": "true",
# "js_scenario": js_scenario,
'country_code':'us'
},
retries=2
)
soup = BeautifulSoup(response.text, 'html.parser')
job_cards = soup.select('.EimVGf')
jobs = []
for card in job_cards:
title = card.select_one('div.tNxQIb.PUpOsf')
company = card.select_one('div.wHYlTd.MKCbgd.a3jPc')
location = card.select_one('div.wHYlTd.FqK3wc.MKCbgd')
posted = card.select_one('span[aria-label$="ago"]')
employment_type = card.select_one('span[aria-label^="Employment"]')
salary = card.select_one('span[aria-label^="Salary"]')
job_link = card.select_one('.MQUd2b')
jobs.append({
"title": title.get_text(strip=True) if title else None,
"company": company.get_text(strip=True) if company else None,
"location_and_portal": location.get_text(strip=True) if location else None,
"posted": posted.get_text(strip=True) if posted else None,
"employment_type": employment_type.get_text(strip=True) if employment_type else None,
"salary": salary.get_text(strip=True) if salary else None,
"job_detail": job_link['href'] if job_link and job_link.has_attr('href') else None
})
return {
'count': len(jobs),
'jobs': jobs,
}
# Run
print(google_jobs_api(position='Software Developer', location='New York, USA'))
After running the scraping script, you can see that it works, but it only shows 10 job postings:
Let's make some adjustments to extract fully rendered job listings by defining additional parameters in the GET API request, and utilize ScrapingBee's extraction engine's better parsing capabilities when working with dynamic JavaScript attributes.
Extracting and Saving Job Data
With your scraper fetching the pages, you now need to extract structured data from them. Using XPath often provides the most reliable way to navigate Google's complex DOM structure.
Pro Tip: To regularly scrape Google job listings over extended periods of time (say, a few weeks to a few months), you’ll also have to keep track of Google’s job listings page structure. Whenever Google updates its HTML code, it could break the CSS or XPath selectors you’re using. If the selectors stop working, you we can always inspect the job card elements in the HTML using the browser’s developer tools and update our selectors accordingly. It is ideal to periodically check the selectors to ensure your scraper works reliably.
To extract the job information, we'll parse the HTML and use CSS or XPath expressions. Instead of parsing with BeautifulSoup, here you will define the extract_rules dictionary, which will be one of the parameters in our GET API call request:
ef google_jobs_api(position='jobs', location="USA"):
extract_rules = {
"jobs": {
"selector": ".EimVGf",
"type": "list",
"output": {
"title": "div.tNxQIb.PUpOsf",
"company": "div.wHYlTd.MKCbgd.a3jPc",
"location_and_portal": "div.wHYlTd.FqK3wc.MKCbgd",
"posted": "span[aria-label$=ago]",
"employment_type": "span[aria-label^=Employment]",
"salary": "span[aria-label^=Salary]",
"job_detail": ".MQUd2b@href"
}
}
}
These CSS expressions target the specific elements containing job information. You might need to adjust them if a Google page changes its structure. They can be redefined using XPath selectors to handle class combinations, nested divs, and partial attribute matches with more precision. For more information, check out our blog on XPath scraping.
To make sure that our API not only takes care of JavaScript rendering but also scrolls through more job listings, we need to define instructions for the js_scenario parameter:
js_scenario = {
"instructions":[
{"evaluate":"(async()=>{let startTime=Date.now();let timeLimit=30000;let lastScrollHeight=0;function sleep(ms){return new Promise(resolve=>setTimeout(resolve,ms));}async function scrollToEnd(){while(Date.now()-startTime<timeLimit){window.scrollTo(0,document.body.scrollHeight);await sleep(1000);let currentScrollHeight=document.body.scrollHeight;if(currentScrollHeight===lastScrollHeight){console.log('Reached the end of the page.');break;}else{lastScrollHeight=currentScrollHeight;}}console.log('Scrolling completed or time limit reached.');}await scrollToEnd();let indicator=document.createElement('div');indicator.id='scrollCompleted';document.body.appendChild(indicator);})();"},
{"wait_for": "#scrollCompleted"}
]
}
Now, integrate it into the list of GET API call parameters. You also added the retries variable to send the request 2 more times if the first one fails.
response = client.get(
f'https://www.google.com/search?q={position}+job+near+{location}&udm=8',
params={
"custom_google": "true",
"stealth_proxy": "true",
"extract_rules": extract_rules,
"js_scenario": js_scenario,
"render_js": "true",
'country_code':'us'
},
retries=2
)
Save results to CSV using pandas
Once you have our job data, you can save it to a CSV file:
job_data = response.json().get('jobs', [])
df = pd.DataFrame(job_data)
file_name = "google_jobs_output.csv"
df.to_csv(file_name, index=False)
return {
'count': len(job_data),
'csv_file': file_name
}
This creates a neatly organized spreadsheet with all your job listings.
Handle multiple queries asynchronously
To speed up your scraping, let's add the async function to run three job queries at the same time. Let's import the async library at the top of the scraping script:
import asyncio
Now define the async function to run multiple google_jobs_api functions.
async def run_all_queries():
loop = asyncio.get_event_loop()
tasks = [
loop.run_in_executor(None, google_jobs_api, "Software Developer", "New York, USA"),
loop.run_in_executor(None, google_jobs_api, "Data Analyst", "San Francisco, USA"),
loop.run_in_executor(None, google_jobs_api, "Marketing Manager", "Austin, USA"),
]
results = await asyncio.gather(*tasks)
This approach will significantly reduce the total time needed to scrape multiple job and location combinations. To learn more about concurrent requests, check out our tutorial on Python async scraping.
Pro Tip: Sending many concurrent requests for job titles or locations can trigger rate limits and unstable responses (like incomplete pages or CAPTCHAs). To maintain stable data collection when scraping larger job datasets, group requests into batches by title or location. Additionally, consider adding a variable time delay between concurrent requests. Stability is paramount for large-scale scraping, even if it means a slight reduction in speed.
Running and Scaling the Scraper
Now let's put everything together and run your scraper at scale. It's a bit like organizing a fleet of ships – you need to coordinate multiple requests while avoiding detection.
import asyncio
from scrapingbee import ScrapingBeeClient
import pandas as pd
import json
client = ScrapingBeeClient(api_key='YOUR_API_KEY')
def google_jobs_api(position='jobs', location="USA"):
extract_rules = {
"jobs": {
"selector": ".EimVGf",
"type": "list",
"output": {
"title": "div.tNxQIb.PUpOsf",
"company": "div.wHYlTd.MKCbgd.a3jPc",
"location_and_portal": "div.wHYlTd.FqK3wc.MKCbgd",
"posted": "span[aria-label$=ago]",
"employment_type": "span[aria-label^=Employment]",
"salary": "span[aria-label^=Salary]",
"job_detail": ".MQUd2b@href"
}
}
}
js_scenario = {
"instructions":[
{"evaluate":"(async()=>{let startTime=Date.now();let timeLimit=30000;let lastScrollHeight=0;function sleep(ms){return new Promise(resolve=>setTimeout(resolve,ms));}async function scrollToEnd(){while(Date.now()-startTime<timeLimit){window.scrollTo(0,document.body.scrollHeight);await sleep(1000);let currentScrollHeight=document.body.scrollHeight;if(currentScrollHeight===lastScrollHeight){console.log('Reached the end of the page.');break;}else{lastScrollHeight=currentScrollHeight;}}console.log('Scrolling completed or time limit reached.');}await scrollToEnd();let indicator=document.createElement('div');indicator.id='scrollCompleted';document.body.appendChild(indicator);})();"},
{"wait_for": "#scrollCompleted"}
]
}
position = position.replace(' ', '+')
location = location.replace(' ', '+')
response = client.get(
f'https://www.google.com/search?q={position.replace(" ","+")}+job+near+{location.replace(" ","+")}&udm=8',
params={
"custom_google": "true",
"stealth_proxy": "true",
"render_js": "true",
"extract_rules": extract_rules,
"js_scenario": js_scenario,
"country_code": "us"
},
retries=2
)
job_data = response.json().get('jobs', [])
df = pd.DataFrame(job_data)
df.to_csv("google_jobs_output.csv", mode='a', index=False, header=False)
return {'count': len(job_data)}
async def run_all_queries():
loop = asyncio.get_event_loop()
tasks = [
loop.run_in_executor(None, google_jobs_api, "Software Developer", "New York, USA"),
loop.run_in_executor(None, google_jobs_api, "Data Analyst", "San Francisco, USA"),
loop.run_in_executor(None, google_jobs_api, "Marketing Manager", "Austin, USA"),
]
results = await asyncio.gather(*tasks)
asyncio.run(run_all_queries())
After a successful extraction, you have a well-structured CSV file that combines concurrent extractions into one data set.
Monitor job status and handle errors
To make your scraper more robust, you can add if statements and except functions for GET API calls when no jobs are found in specific locations. Other preventive measures include the retries parameter, that does not stop the scraper after one unsuccessful connection request.
These changes ensure that each URL is targeted multiple times before giving up, which helps handle temporary network issues or rate limiting.
Scaling Google Jobs Scraping for Multiple Queries
Say you want to scrape Google job listings for 100 job titles (e.g., backend engineer, frontend engineer, Python engineer) across 1,000 locations (California, Texas, Washington). This task easily compounds to 100x1,000 = 100,000 requests. To automate and scale efficiently, you need to use concurrent requests, sending multiple requests at once to scrape Google jobs results and then aggregating everything into one dataset. Each scraping request involves network time to fetch data and local processing time to parse the HTML and extract your job data. Sending requests concurrently means the network activity continues without waiting for local processing to finish, significantly improving scraping efficiency.
By rotating a set of job titles with a set of locations, you can gather extensive job postings data for comprehensive job market analysis and recruitment research. This approach is key to effective Google Jobs scraping.
What Data Can You Extract: Understanding Google Jobs Structure
To effectively scrape Google Jobs, it helps to understand how the data is structured. Google Jobs data is rendered dynamically with JavaScript, which is why ScrapingBee's render_js feature is essential for accessing the complete information.
Extract What Google Jobs displays in listings
When you search for jobs on Google, the results appear in a dedicated "Jobs" widget that contains cards for each position. These cards show a preview of the job information, and clicking on one expands it to reveal more details like the full description, application link, and posting date.
The most valuable data points from Google Jobs listings include:
Job title
Company name
Location
Salary (when available)
Job description snippet
Posted date
Application link
Tips for scaling to more locations
If you're not getting locations:
*Use state/province names for broader searches
*Add radius qualifiers like "within 50 miles" for rural areas
*Rotate through different search patterns to avoid detection
Common Challenges When Web Scraping Google Jobs
It is not as easy to scrape Google Jobs as it is to scrape a generic, plain HTML website. Firstly, much of the data you need is dynamically rendered with JavaScript. Moreover, the page initially loads only a handful of listings, and more listings appear only as the user scrolls down. Finally, Google also tries to stop bots, meaning you need to appear like a human user, and also rotate proxies or IPs to avoid detection.
So, how to scrape Google careers with these challenges? In your Google jobs scraping Python script, you used ”render_js”: “true”, to get output from a browser rendering the JavaScript loaded content, and implemented an automated scroll using the js_scenario feature. You also used a stealth proxy with multiple retries to overcome anti-bot protections.
Scrape Google Jobs Results with Confidence
By now, you should have a fully functional Google Jobs scraper powered by ScrapingBee. The beauty of this approach is that you don't have to worry about proxy rotation, browser fingerprinting, or JavaScript rendering – ScrapingBee handles all of that behind the scenes. This means you can focus on extracting and analyzing the job data rather than fighting with anti-scraping measures.
Ready to start collecting job data? Grab your API key today and run your first scraper. And if you run into any challenges, our detailed documentation and support team are here to help.
Google Jobs Scraping FAQs
Does Google Jobs Have an API?
At the time of writing, there seems to be no official Google Jobs API that allows us to get the jobs indexed by Google in JSON/XML format. However, there are third-party services you can use to scrape Google Jobs from the results HTML page, such as ScrapingBee’s service.
Can Google Jobs listings change depending on location?
Yes. Google Jobs listings change based on location. Most roles are tied to a specific city, region, or “remote,” and Google shows results that match the user’s location settings and the location mentioned in the query.
Do Google Jobs listings come directly from company websites?
Google Jobs listings are usually from the source where they were originally picked up. So, if the company posts job listings on its website, Google could pick them up from there. Otherwise, the company could use a dedicated ATS (Applicant Tracking System) such as Ashby or Lever, a social media platform such as LinkedIn, or have a recruitment agency post a job listing on their behalf elsewhere. Google could show jobs indexed from any of these sources too.
Can Google Jobs results vary between different search queries?
Yes, Google jobs results vary between queries, typically based on the semantic relation to the job title the user searches for and also the location mentioned in the query (if any). For example, “Chef jobs in New York” and “Engineer jobs near Houston” would return very different results.
Is scraping job posting legal?
Scraping job postings for personal use, research, or aggregation with proper attribution is generally acceptable. However, you should always respect robots.txt files and avoid overloading servers with requests. Additionally, consider the terms of service of the specific job platforms you're accessing through Google Jobs.
Before you go, check out these related reads:
Kevin worked in the web scraping industry for 10 years before co-founding ScrapingBee. He is also the author of the Java Web Scraping Handbook.