How to Scrape LinkedIn Data in 2025

In this guide, you will learn:

What a LinkedIn scraper is
A comparison between LinkedIn web scraping and retrieving data via its APIs
How to bypass the LinkedIn login wall
How to build a LinkedIn scraping script in Python
How to get LinkedIn data with a simpler and more effective solution

Let’s dive in!

What Is a LinkedIn Scraper?

A LinkedIn scraper is a tool that automatically extracts data from LinkedIn pages. It typically targets popular pages such as profiles, job listings, company pages, and articles.

The scraper collects key information from those pages and presents it in useful formats like CSV or JSON. This data is valuable for lead generation, job searching, analyzing competitors, and identifying market trends, among other use cases.

LinkedIn Web Scraping vs LinkedIn API

LinkedIn provides an official API that allows developers to integrate with the platform and retrieve some data. So, why should you even consider LinkedIn web scraping? The answer is simple and involves four key points:

The API only returns a subset of data defined by LinkedIn, which may be much smaller than the data available through web scraping.
APIs can change over time, limiting the control you have over the data you can access.
LinkedIn’s API is primarily focused on marketing and sales integrations, especially for free-tier users.
The LinkedIn API can cost dozens of dollars per month, but it still has strict limitations on the data and the number of profiles you can retrieve data from.

The comparison between the two approaches to getting LinkedIn data leads to the following summary table:

Aspect	LinkedIn API	LinkedIn Web Scraping
Data availability	Limited to a subset of data defined by LinkedIn	Access to all publicly available data on the site
Control over data	LinkedIn controls the data provided	Full control over the data you retrieve
Focus	Primarily for marketing and sales integrations	Can target any LinkedIn page
Cost	Can cost dozens of dollars per month	No direct cost (except for infrastructure)
Limitations	Limited number of profiles and data per month	No strict limitations

For more information, read our guide on web scraping vs API.

What Data to Scrape From LinkedIn

Here is a partial list of the types of data you can scrape from LinkedIn:

Profile: personal details, work experience, education, connections, etc.
Companies: company information, employee lists, job postings, etc.
Job listings: job descriptions, applications, criteria, etc.
Job positions: job title, company, location, salary, etc.
Articles: published posts, articles written by users, etc.
LinkedIn Learning: courses, certifications, learning paths, etc.

How to Avoid the LinkedIn Login Wall

If you try to visit the LinkedIn Jobs page directly after a Google search in incognito mode (or while logged out), this is what you should get:

The above page may lead you to believe that job searching on LinkedIn is only possible after logging in. Since scraping data behind a login wall might lead to legal issues as it can violate LinkedIn’s terms of service, you want to avoid that.

Luckily, there is a simple workaround to access the jobs page without being blocked. All you need to do is visit the LinkedIn homepage and click on the “Jobs” tab:

Clicking on the “Jobs” link on LinkedIn’s homepage

This time, you will get access to the job search page:

As you can see in the URL bar of your browser, this time, the URL in your browser contains special query parameters:

https://www.linkedin.com/jobs/search?trk=guest_homepage-basic_guest_nav_menu_jobs&position=1&pageNum=0

In particular, the trk=guest_homepage-basic_guest_nav_menu_jobs argument appears to be the key factor preventing LinkedIn from enforcing a login wall.

However, that does not mean you can access all LinkedIn data. Some sections still require you to be logged in. Yet, as we are about to see, that is not a major limitation for LinkedIn web scraping.

Build a LinkedIn Web Scraping Script: Step-By-Step Guide

In this tutorial section, you will learn how to scrape job posting data for software engineer positions in New York from LinkedIn:

We will start from the search results page, automatically retrieve job listing URLs, and then extract data from their detail pages. The LinkedIn scraper will be written in Python, one of the best programming languages for web scraping.

Let’s perform LinkedIn data scraping with Python!

Step #1: Project Setup

Before getting started, make sure you have Python 3 installed on your machine. Otherwise, download it and follow the installation wizard.

Now, use the command below to create a folder for your scraping project:

mkdir linkedin-scraper

linkedin-scraper represents the project folder of your Python LinkedIn scraper.

Enter it, and initialize a virtual environment within it:

cd linkedin-scraper
python -m venv venv

Load the project folder in your favorite Python IDE. Visual Studio Code with the Python extension or PyCharm Community Edition will do.

Create a scraper.py file in the project’s folder, which should contain this file structure:

The file structure of the LinkedIn Scraper

Right now, scraper.py is a blank Python script but it will soon contain the desired scraping logic.

In the IDE’s terminal, activate the virtual environment. In Linux or macOS, launch this command:

./env/bin/activate

Equivalently, on Windows, execute:

env/Scripts/activate

Wonderful! You now have a Python environment for web scraping.

Step #2: Selection and Installation of the Scraping Libraries

Before diving into coding, you must analyze the target site to determine the right scraping tools for the job.

Start by opening the LinkedIn Jobs search page in incognito mode as explained earlier. Using incognito mode ensures that you are logged out and that no cached data interferes with the scraping process.

This is what you should see:

LinkedIn displays several popups in the browser. These can be annoying to deal with when using browser automation tools like Selenium or Playwright.

Fortunately, if you inspect the HTML source code of the page returned by the server, you will see that it already contains most of the data on the page:

Similarly, if you check the “Network” tab in DevTools, you will notice that the page does not rely on significant dynamic API calls:

In other words, most of the content on LinkedIn job pages is static. That means you do not need a browser automation tool to scrape LinkedIn. A combination of an HTTP client and an HTML parser will be enough to retrieve job data.

Thus, we will use two Python libraries to scrape LinkedIn jobs:

Requests: A simple HTTP library for sending GET requests and retrieving web page content.
Beautiful Soup: A powerful HTML parser that makes it easy to extract data from web pages.

In an activated virtual environment, install both libraries with:

pip install requests beautifulsoup4

Then, import them in scraper.py with:

from bs4 import BeautifulSoup
import requests

Great! You now have everything you need to start scraping LinkedIn.

Step #3: Structure the Scraping Script

As explained in the introduction of this section, the LinkedIn scraper will perform two main tasks:

Retrieve job page URLs from the LinkedIn Jobs search page
Extract job details from each specific job page

To keep the script well-organized, structure your scraper.py file with two functions:

def retrieve_job_urls(job_search_url):
    pass
    # To be implemented...

def scrape_job(job_url):
    pass
    # To be implemented...

# Function calls and data export logic...

Below is what the two functions do:

retrieve_job_urls(job_search_url): Accepts the job search page URL and returns a list of job page URLs.
scrape_job(job_url): Accepts a job page URL and extracts job details such as title, company, location, and description.

Then, at the end of the script, call these functions and implement the data export logic to store the scraped job data. Time to implement that logic!

Step #4: Connect to the Job Search Page

In the retrieve_job_urls() function, use the requests library to fetch the target page using the URL passed as an argument:

response = requests.get(job_url)

Behind the scenes, this performs an HTTP GET request to the target page and retrieves the HTML document returned by the server.

To access the HTML content from the response, use the .text attribute:

html = response.text

Amazing! You are now ready to parse the HTML and start extracting job URLs.

Step #5: Retrieve Job URLs

To parse the HTML retrieved earlier, pass it to the Beautiful Soup constructor:

soup = BeautifulSoup(html, "html.parser")

The second argument specifies the HTML parser to use. html.parser is the default parser included in Python’s standard library.

Next, inspect the job card elements on the LinkedIn job search page. Right-click on one of it and select “Inspect” in your browser’s DevTools:

As you can see in the HTML code of the job card, job URLs can be extracted using the following CSS selector:

[data-tracking-control-name="public_jobs_jserp-result_search-card"]

Note: Using data-* attributes for node selection in web scraping is ideal because those are often used for testing or internal tracking. That makes them less likely to change over time.

Now, if you take a look at the page, you will see that some job cards may appear blurred behind a login invitation element:

The blurred job cards have the same HTML structure as the non-blurred ones

No need to worry, as that is just a frontend effect. The underlying HTML still contains the job page URLs, so you can get those URLs without logging in.

Here is the logic to extract job posting URLs from the LinkedIn Jobs page:

job_urls = []

job_url_elements = soup.select("[data-tracking-control-name=\"public_jobs_jserp-result_search-card\"]")
for job_url_element in job_url_elements:
  job_url = job_url_element["href"]
  job_urls.append(job_url)

select() returns all elements matching the given CSS selector, which contain job links. Then, the script:

Iterates through these elements
Access the href HTML attribute (the job page URL)
Appends it to the job_urls list

If you are not familiar with the above logic, read our guide on Beautiful Soup web scraping.

At the end of this step, your retrieve_job_urls() function will look like this:

def retrieve_job_urls(job_search_url):
    # Make an HTTP GET request to get the HTML of the page
    response = requests.get(job_search_url)

    # Access the HTML and parse it
    html = response.text
    soup = BeautifulSoup(html, "html.parser")

    # Where to store the scraped data
    job_urls = []

    # Scraping logic
    job_url_elements = soup.select("[data-tracking-control-name=\"public_jobs_jserp-result_search-card\"]")
    for job_url_element in job_url_elements:
      # Extract the job page URL and append it to the list
      job_url = job_url_element["href"]
      job_urls.append(job_url)

    return job_urls

You can call this function on the target page as below:

public_job_search_url = "https://www.linkedin.com/jobs/search?keywords=Software%2BEngineer&location=New%20York%2C%20New%20York%2C%20United%20States&geoId=102571732&trk=public_jobs_jobs-search-bar_search-submit&position=1&pageNum=0"
job_urls = retrieve_job_urls(public_job_search_url)

Good job! You have now completed the first task of your LinkedIn scraper.

Step #6: Initialize the Job Data Scraping Task

Now, focus on the scrape_job() function. Just like before, use the requests library to fetch the HTML of the job page from the provided URL and parse it with Beautiful Soup:

response = requests.get(job_url)

html = response.text
soup = BeautifulSoup(html, "html.parser")

Since scraping LinkedIn job data involves extracting various pieces of information, we will break it down into two steps to simplify the process.

Step #7: Job Data Retrieval — Part 1

Before jumping into LinkedIn data scraping, you need to inspect a job posting detail page to understand what data it contains and how to retrieve it.

To do that, open a job position page in incognito mode and use the DevTools to inspect the page. Focus on the top section of the job posting page:

The HTML code of the top section of the page

Note that you can extract the following data:

The job position title from the <h1> tag
The company offering the position from the [data-tracking-control-name="public_jobs_topcard-org-name"] element
The location information from .topcard__flavor--bullet
The number of applicants from the .num-applicants__caption node

In the scrape_job() function, after parsing the HTML, use the following logic to extract those fields:

title_element = soup.select_one("h1")
title = title_element.get_text().strip()

company_element = soup.select_one("[data-tracking-control-name=\"public_jobs_topcard-org-name\"]")
company_name = company_element.get_text().strip()
company_url = company_element["href"]

location_element = soup.select_one(".topcard__flavor--bullet")
location = location_element.get_text().strip()

applicants_element = soup.select_one(".num-applicants__caption")
applicants = applicants_element.get_text().strip()

The strip() function is required to remove any leading or trailing spaces from the scraped text.

Next, focus on the salary section of the page:

You can retrieve this info through the .salary CSS selector. Since not all job positions have this section, you need extra logic to check whether the HTML element is present on the page:

salary_element = soup.select_one(".salary")
if salary_element is not None:
    salary = salary_element.get_text().strip()
else:
    salary = None

When .salary is not on the page, select_one() will return None, and the salary variable will be set to None.

Terrific! You just extracted part of the data from a LinkedIn job position page.

Step #8: Job Data Retrieval — Part 2

Now, focus on the lower section of the LinkedIn job position page, starting with the job description:

You can access the job description text from the .description__text .show-more-less-html element using this code:

description_element = soup.select_one(".description__text .show-more-less-html")
description = description_element.get_text().strip()

Finally, the most challenging part of LinkedIn web scraping is dealing with the criteria section:

In this case, you cannot predict exactly what data will be present on the page. Thus, you need to treat criterium each entry as a <name, value> pair. To scrape the criteria data:

Select the <ul> element using the .description__job-criteria-list li CSS selector
Iterate over the selected elements, and for each one:
1. Scrape the item name from .description__job-criteria-subheader
2. Scrape the item value from .description__job-criteria-text
3. Append the scraped data as a dictionary to an array

Implement the LinkedIn scraping logic for the criteria section with these lines of code:

criteria = []
criteria_elements = soup.select(".description__job-criteria-list li")
for criteria_element in criteria_elements:
    name_element = criteria_element.select_one(".description__job-criteria-subheader")
    name = name_element.get_text().strip()

    value_element = criteria_element.select_one(".description__job-criteria-text")
    value = value_element.get_text().strip()

    criteria.append({
        "name": name,
        "value": value
    })

Perfect! You just successfully scraped the job data. The next step is to collect all the scraped LinkedIn data into an object and return it.

Step #9: Collect the Scraped Data

Use the scraped data from the previous two steps to populate a job object and return it from the function:

job = {
    "url": job_url,
    "title": title,
    "company": {
        "name": company_name,
        "url": company_url
    },
    "location": location,
    "applications": applicants,
    "salary": salary,
    "description": description,
    "criteria": criteria
}
return job

After completing the previous three steps, scrape_job() should look like this:

def scrape_job(job_url):
    # Send an HTTP GET request to fetch the page HTML
    response = requests.get(job_url)

    # Access the HTML text from the response and parse it
    html = response.text
    soup = BeautifulSoup(html, "html.parser")

    # Scraping logic
    title_element = soup.select_one("h1")
    title = title_element.get_text().strip()

    company_element = soup.select_one("[data-tracking-control-name=\"public_jobs_topcard-org-name\"]")
    company_name = company_element.get_text().strip()
    company_url = company_element["href"]

    location_element = soup.select_one(".topcard__flavor--bullet")
    location = location_element.get_text().strip()

    applicants_element = soup.select_one(".num-applicants__caption")
    applicants = applicants_element.get_text().strip()

    salary_element = soup.select_one(".salary")
    if salary_element is not None:
      salary = salary_element.get_text().strip()
    else:
       salary = None

    description_element = soup.select_one(".description__text .show-more-less-html")
    description = description_element.get_text().strip()

    criteria = []
    criteria_elements = soup.select(".description__job-criteria-list li")
    for criteria_element in criteria_elements:
        name_element = criteria_element.select_one(".description__job-criteria-subheader")
        name = name_element.get_text().strip()

        value_element = criteria_element.select_one(".description__job-criteria-text")
        value = value_element.get_text().strip()

        criteria.append({
            "name": name,
            "value": value
        })

    # Collect the scraped data and return it
    job = {
        "url": job_url,
        "title": title,
        "company": {
            "name": company_name,
            "url": company_url
        },
        "location": location,
        "applications": applicants,
        "salary": salary,
        "description": description,
        "criteria": criteria
    }
    return job

To collect the job data, call this function by iterating over the job URLs returned by retrieve_job_urls(). Then, append the scraped data to a jobs array:

jobs = []
for job_url in job_urls:
    job = scrape_job(job_url)
    jobs.append(job)

Fantastic! The LinkedIn data scraping logic is now complete.

Step #10: Export to JSON

The job data extracted from LinkedIn is now stored in an array of objects. Since these objects are not flat, it makes sense to export this data in a structured format like JSON.

Python allows you to export data to JSON without needing any extra dependencies. To do so, you can use the following logic:

file_name = "jobs.json"
with open(file_name, "w", encoding="utf-8") as file:
    json.dump(jobs, file, indent=4, ensure_ascii=False)

The open() function creates the jobs.json output file, which is then populated with json.dump(). The indent=4 ensures the JSON is formatted for readability, and ensure_ascii=False ensures that non-ASCII characters are correctly encoded.

To make the code work, do not forget to import json from the Python Standard Library:

import json

Step #11: Finalize the Scraping Logic

Now, the LinkedIn scraping script is basically complete. Still, there are a few improvements you can make:

Limit the number of jobs scraped
Add some logging to monitor the script’s progress

The first point is important because:

You do not want to overwhelm the target server with too many requests from your LinkedIn scraper
You do not know how many jobs are on a single page

So, it makes sense to limit the number of jobs scraped as follows:

scraping_limit = 10
jobs_to_scrape = job_urls[:scraping_limit]

jobs = []
for job_url in jobs_to_scrape:
    # ...

This will limit the scraped pages to up to 10.

Then, add some print() statements to monitor what the script is doing while it is running:

public_job_search_url = "https://www.linkedin.com/jobs/search?keywords=Software%2BEngineer&location=New%20York%2C%20New%20York%2C%20United%20States&geoId=102571732&trk=public_jobs_jobs-search-bar_search-submit&position=1&pageNum=0"

print("Starting job retrieval from LinkedIn search URL...")

job_urls = retrieve_job_urls(public_job_search_url)

print(f"Retrieved {len(job_urls)} job URLs\n")

scraping_limit = 10
jobs_to_scrape = job_urls[:scraping_limit]

print(f"Scraping {len(jobs_to_scrape)} jobs...\n")

jobs = []
for job_url in jobs_to_scrape:
    print(f"Starting data extraction on \"{job_url}\"")

    job = scrape_job(job_url)
    jobs.append(job)

    print(f"Job scraped")

print(f"\nExporting {len(jobs)} scraped jobs to JSON")
file_name = "jobs.json"
with open(file_name, "w", encoding="utf-8") as file:
    json.dump(jobs, file, indent=4, ensure_ascii=False)

print(f"Jobs successfully saved to \"{file_name}\"\n")

The logging logic will help you track the scraper’s progress, which is essential considering that it goes through several steps.

Step #12: Put It All Together

This is the final code of your LinkedIn scraping script:

from bs4 import BeautifulSoup
import requests
import json

def retrieve_job_urls(job_search_url):
    # Make an HTTP GET request to get the HTML of the page
    response = requests.get(job_search_url)

    # Access the HTML and parse it
    html = response.text
    soup = BeautifulSoup(html, "html.parser")

    # Where to store the scraped data
    job_urls = []

    # Scraping logic
    job_url_elements = soup.select("[data-tracking-control-name=\"public_jobs_jserp-result_search-card\"]")
    for job_url_element in job_url_elements:
      # Extract the job page URL and append it to the list
      job_url = job_url_element["href"]
      job_urls.append(job_url)

    return job_urls

def scrape_job(job_url):
    # Send an HTTP GET request to fetch the page HTML
    response = requests.get(job_url)

    # Access the HTML text from the response and parse it
    html = response.text
    soup = BeautifulSoup(html, "html.parser")

    # Scraping logic
    title_element = soup.select_one("h1")
    title = title_element.get_text().strip()

    company_element = soup.select_one("[data-tracking-control-name=\"public_jobs_topcard-org-name\"]")
    company_name = company_element.get_text().strip()
    company_url = company_element["href"]

    location_element = soup.select_one(".topcard__flavor--bullet")
    location = location_element.get_text().strip()

    applicants_element = soup.select_one(".num-applicants__caption")
    applicants = applicants_element.get_text().strip()

    salary_element = soup.select_one(".salary")
    if salary_element is not None:
      salary = salary_element.get_text().strip()
    else:
       salary = None

    description_element = soup.select_one(".description__text .show-more-less-html")
    description = description_element.get_text().strip()

    criteria = []
    criteria_elements = soup.select(".description__job-criteria-list li")
    for criteria_element in criteria_elements:
        name_element = criteria_element.select_one(".description__job-criteria-subheader")
        name = name_element.get_text().strip()

        value_element = criteria_element.select_one(".description__job-criteria-text")
        value = value_element.get_text().strip()

        criteria.append({
            "name": name,
            "value": value
        })

    # Collect the scraped data and return it
    job = {
        "url": job_url,
        "title": title,
        "company": {
            "name": company_name,
            "url": company_url
        },
        "location": location,
        "applications": applicants,
        "salary": salary,
        "description": description,
        "criteria": criteria
    }
    return job

# The public URL of the LinkedIn Jobs search page
public_job_search_url = "https://www.linkedin.com/jobs/search?keywords=Software%2BEngineer&location=New%20York%2C%20New%20York%2C%20United%20States&geoId=102571732&trk=public_jobs_jobs-search-bar_search-submit&position=1&pageNum=0"

print("Starting job retrieval from LinkedIn search URL...")

# Retrieving the single URLs for each job on the page
job_urls = retrieve_job_urls(public_job_search_url)

print(f"Retrieved {len(job_urls)} job URLs\n")

# Scrape only up to 10 jobs from the page
scraping_limit = 10
jobs_to_scrape = job_urls[:scraping_limit]

print(f"Scraping {len(jobs_to_scrape)} jobs...\n")

# Scrape data from each job position page
jobs = []
for job_url in jobs_to_scrape:
    print(f"Starting data extraction on \"{job_url}\"")

    job = scrape_job(job_url)
    jobs.append(job)

    print(f"Job scraped")

# Export the scraped data to CSV
print(f"\nExporting {len(jobs)} scraped jobs to JSON")
file_name = "jobs.json"
with open(file_name, "w", encoding="utf-8") as file:
    json.dump(jobs, file, indent=4, ensure_ascii=False)

print(f"Jobs successfully saved to \"{file_name}\"\n")

Launch it with the following command:

python scraper.py

The LinkedIn scraper should log the following information:

Starting job retrieval from LinkedIn search URL...
Retrieved 60 job URLs                                                                                                        
Scraping 10 jobs...

Starting data extraction on "https://www.linkedin.com/jobs/view/software-engineer-recent-graduate-at-paypal-4149786397?position=1&pageNum=0&refId=nz9sNo7HULREru1eS2L9nA%3D%3D&trackingId=uswFC6EjKkfCPcv0ykaojw%3D%3D"
Job scraped

# omitted for brevity...

Starting data extraction on "https://www.linkedin.com/jobs/view/software-engineer-full-stack-at-paces-4090771382?position=2&pageNum=0&refId=UKcPcvFZMOsZrn0WhZYqtg%3D%3D&trackingId=p6UUa6cgbpYS1gDkRlHV2g%3D%3D"
Job scraped

Exporting 10 scraped jobs to JSON
Jobs successfully saved to "jobs.json"

Up to the 60 job pages it found, the script only scraped 10 jobs. Therefore, the jobs.json output file generated by the script will contain exactly 10 job positions:

[
    {
        "url": "https://www.linkedin.com/jobs/view/software-engineer-recent-graduate-at-paypal-4149786397?position=1&pageNum=0&refId=UKcPcvFZMOsZrn0WhZYqtg%3D%3D&trackingId=UzOyWl8Jipb1TFAGlLJxqw%3D%3D",
        "title": "Software Engineer - Recent Graduate",
        "company": {
            "name": "PayPal",
            "url": "https://www.linkedin.com/company/paypal?trk=public_jobs_topcard-org-name"
        },
        "location": "New York, NY",
        "applications": "Over 200 applicants",
        "salary": null,
        "description": "Omitted for brevity...",
        "criteria": [
            {
                "name": "Seniority level",
                "value": "Not Applicable"
            },
            {
                "name": "Employment type",
                "value": "Full-time"
            },
            {
                "name": "Job function",
                "value": "Engineering"
            },
            {
                "name": "Industries",
                "value": "Software Development, Financial Services, and Technology, Information and Internet"
            }
        ]
    },

    // other 8 positions...

    {
        "url": "https://www.linkedin.com/jobs/view/software-engineer-full-stack-at-paces-4090771382?position=2&pageNum=0&refId=UKcPcvFZMOsZrn0WhZYqtg%3D%3D&trackingId=p6UUa6cgbpYS1gDkRlHV2g%3D%3D",
        "title": "Software Engineer (Full-Stack)",
        "company": {
            "name": "Paces",
            "url": "https://www.linkedin.com/company/pacesai?trk=public_jobs_topcard-org-name"
        },
        "location": "Brooklyn, NY",
        "applications": "Over 200 applicants",
        "salary": "$150,000.00/yr - $200,000.00/yr",
        "description": "Omitted for brevity...",
        "criteria": [
            {
                "name": "Seniority level",
                "value": "Entry level"
            },
            {
                "name": "Employment type",
                "value": "Full-time"
            },
            {
                "name": "Job function",
                "value": "Engineering and Information Technology"
            },
            {
                "name": "Industries",
                "value": "Software Development"
            }
        ]
    },
]

Et voilà! Web scraping LinkedIn in Python is not that difficult.

Streamline LinkedIn Data Scraping

The script we built might make LinkedIn scraping seem like a simple task, but that is not the case. The login wall and data obfuscation techniques can quickly become challenges, especially as you scale your scraping operation.

Additionally, LinkedIn has rate-limiting mechanisms in place to block automated scripts making too many requests. A common workaround is to rotate your IP address in Python, but that requires additional effort.

Plus, keep in mind that LinkedIn is constantly evolving, so the maintenance efforts and costs for your scraper are not negligible. LinkedIn holds a treasure trove of data in various formats, from job posts to articles. To retrieve all this information, you would need to build different scrapers and manage them all.

Forget about the hassle with Bright Data’s LinkedIn Scraper API. This dedicated tool can scrape all the LinkedIn data you need and deliver it via no-code integrations or through simple endpoints that you can call with any HTTP client.

Use it to scrape LinkedIn profiles, posts, companies, jobs, and much more in seconds—without having to manage an entire scraping architecture.

Conclusion

In this step-by-step tutorial, you learned what a LinkedIn scraper is and the types of data it can retrieve. You also built a Python script to scrape job postings from LinkedIn.

The challenge is that LinkedIn uses IP bans and login walls to block automated scripts. Skip these problems with our LinkedIn Scraper.

If web scraping is not for you but you are still interested in the data, check out our ready-to-use LinkedIn datasets!

Create a free Bright Data account today to try our scraper APIs or explore our datasets.

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How to Scrape LinkedIn: 2025 Guide