The job market changes at a rapid pace. This makes the job market dynamic and requires employees and employers to act quickly. Staying informed is the key to grabbing potential opportunities. However, it could be hard for some to keep track of all the data. This is because the changes happen much more quickly. This is the area where job scraping becomes relevant.

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In this guide, we have discussed the best way to scrape job postings in 2024. So, let’s see what we have got here!

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What is Job Posting Data?

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This is the data that you normally see in job advertisements or listings. The ads are published by the employers to attract potential employees. The information provided in a job post can differ depending on the platform.

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Here is the most common type of data that you can find in a job posting.

• Job titles & job description
• Job qualifications and requirements
• Relevant company information
• Job salary and benefits

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Why Scrape Job Data?

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Job data is among the most critical sets of data when it comes to web data scraping or job data extraction. Looking at the job listings and retrieving relevant information is a daunting task. However, the hard work pays off in a variety of ways.

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Here are the reasons why you should consider scraping job data:

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Aggregating Job Listings

Job scraping makes it easy for you to aggregate data from different job listings and organize them into a centralized location. It offers you an extensive overview of available job listings on multiple job boards.

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Automation

The job posting data can be extracted automatically. This suggests that companies can quickly go through a considerable number of job posts within less time and get relevant information about the prevailing job trends.

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Better Job Matching

Scraping job boards is an efficient way to find better jobs. This enables job seekers to locate positions that align with their experience and skill level.

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Quick Updates

By using an automated job scraping process, companies can easily collect web data from multiple sources. They can keep their extracted data updated with regular updates.

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Analyzing Competition

Companies scrape job postings with a view to analyzing and monitoring the job market trends. This helps them ascertain what types of skills are in demand. Companies can also find out about the prevailing salary trends. As a result, organizations can make informed decisions when it comes to acquiring new talent.

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Improved Recruiting Strategies

Job scraping relies on a wide range of tools to help automate the recruitment process. This ensures that only qualified people are selected for the job.

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What are the Ways to Scrape Job Boards

No doubt, scraping job postings can take hours. In most cases, the websites use various tools and software to prevent scraping data. Some of the most widely used techniques include dynamic content and CAPTCHA. This suggests your proxies could be blacklisted or blocked in no time.

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Websites are now well-protected against automated activities. On the other hand, companies and individuals involved in scraping data are also finding new ways to retrieve data without leaving any footprints.

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Hence, you can certainly use effective ways to minimize the risk of blocking your proxies. You can do this ethically without breaching the data scraping regulations. When scraping the job boards, make sure to do it the right way.

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The major challenges associated with job data scraping relate to the methods of retrieving data. Here are a few of the most prominent methods of scraping job boards:

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• Building an in-house Job Board Scraper
• Buying a Pre-Built Scraper
• Buying Job Databases

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Building an In-House Job Board Scraper

Creating and maintaining an in-house job board scraper is an expensive affair. The cost could skyrocket if you don’t have a data analysis or development team. However, using this method relieves you from the hassle of receiving required data from a third-party provider.

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Advantages

• Offers better adaptability options to help you scrape specific data.
• It allows you to have complete control over the scraping infrastructure.

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Disadvantages

• An in-house job board scraper tends to be resource-hungry.

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Buying a Pre-Built Scraper

Using a pre-built scraper can save some time and cost. With this method, you don’t have to invest in maintaining your in-house scraper. This suggests you will be taking advantage of someone else’s platform.

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Advantages

• Minimizes maintenance and development expenses.
• Ability to scale up the job scraper as per requirement.

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Disadvantages

• You can’t exercise complete control over a scraping tool.

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Buying Job Databases

This is among the most hassle-free methods for scraping job boards. It allows purchasing pre-scraped job datasets from service providers offering job scraping data.

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Advantages

• Easy to use.
• Requires no resources for development.

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Disadvantages

• Fails to offer control over the data scraping process.
• They could end up buying outdated data.

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Building an in-house job board scraper is the most effective of the above three options. That’s why we are explaining this method further.

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Building Personal Job Boards Scraper

When it comes to building and setting up an in-house job scraping tool, you need to consider the following aspects:

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Find out which APIs, languages, libraries, and frameworks are widely used. This will save you time when making development changes in the future.

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Data storage could be a problem, which means you should consider buying sufficient storage space.

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Rely on a stable and efficient testing environment. This is so, as while building a job scraper, you will face similar challenges.

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These are a few of the basic guidelines that you need to consider when creating an in-house job board scraper. Below, we have explained how to scrape job listings with ScrapIn API and Python.

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1. Set Up Your Environment

To start with, go to the official Python website and download the setup. In addition, you can also use an integrated development environment (IDE) such as Visual Studio Code, PyCharm, or similar.

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Once everything is ready, go to the terminal and install the requests library using pip, the Python package installer:

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python -m pip install requests

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After that, you need to create a new Python file and import these libraries:

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import requests, json, csv

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The requests library will allow you to send HTTP requests to the API. In addition, the CSV and JSON libraries will process the parsed and scraped data.

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2. Get a Free API Trial

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ScrapIn API comes with a free trial, so head to the dashboard and register with a free account.

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Once you have created an account and copy your API key, make sure to store this information in the following way:

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API_KEY = 'YOUR_API_KEY'  # Replace with your API key from the Scrapin dashboard

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3. Send a Request to the API

The next step involves creating a response object, which sends a GET request to the API. You can do this while using the API key for authentication. As a result, you can allow the payload to pass as a JSON object:

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API_KEY = 'YOUR_API_KEY'  # Replace with your API key from the Scrapin dashboard
LINKEDIN_COMPANY_URL = 'LINKEDIN_COMPANY_URL'  # Replace with the LinkedIn company URL

endpoint = f"https://api.scrapin.io/enrichment/company/jobs?apikey={API_KEY}&linkedinUrl={LINKEDIN_COMPANY_URL}"

response = requests.get(endpoint)
response.raise_for_status()
data = response.json()

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After the API returns a response, you need to parse it by accessing the scraped content from the results > content keys. Once done, you should use the JSON module to load the data as a Python dictionary.

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4. Save JSON to a CSV file

For superior readability, you should consider saving the scraped and parsed results to a CSV file, which supports the Excel format. You can achieve this by using a built-in CSV:

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# Save the entire JSON response to a file
        with open('jobs_response.json', 'w') as json_file:
            json.dump(data, json_file, indent=4)
        print("Full JSON response saved to jobs_response.json")

        # Check if there are jobs in the response and proceed with saving to CSV
        if data['success'] and 'openJobs' in data:
            jobs = data['openJobs']

            # Specify the CSV file and field names
            with open('jobs.csv', mode='w', newline='') as file:
                fieldnames = ['jobId', 'title', 'location', 'description', 'postedDate', 'employmentType', 'applyUrl']
                writer = csv.DictWriter(file, fieldnames=fieldnames)

                # Write headers and job rows
                writer.writeheader()
                for job in jobs:
                    writer.writerow({
                        'jobId': job['jobId'],
                        'title': job['title'],
                        'location': job['location'],
                        'description': job['description'],
                        'postedDate': job['postedDate'],
                        'employmentType': job['employmentType'],
                        'applyUrl': job['applyUrl']
                    })

            print("Jobs saved successfully to jobs.csv")
        else:
            print("No jobs found in the response.")
    except requests.exceptions.RequestException as error:
        print('Error:', error)

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You can also provide some additional instructions to extract more information about the precise geolocation, tools, or similar other details.

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Once you have finished running the code, you will find a jobs.csv file saved in your directory.

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The final Python file should be:

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import requests, csv, json

API_KEY = 'YOUR_API_KEY'  # Replace with your API key from the Scrapin dashboard
LINKEDIN_COMPANY_URL = 'LINKEDIN_COMPANY_URL'  # Replace with the LinkedIn company URL

def get_linkedin_jobs():
    endpoint = f"https://api.scrapin.io/enrichment/company/jobs?apikey={API_KEY}&linkedinUrl={LINKEDIN_COMPANY_URL}"

    try:
        response = requests.get(endpoint)
        response.raise_for_status()
        data = response.json()

        # Save the entire JSON response to a file
        with open('jobs_response.json', 'w') as json_file:
            json.dump(data, json_file, indent=4)
        print("Full JSON response saved to jobs_response.json")

        # Check if there are jobs in the response and proceed with saving to CSV
        if data['success'] and 'openJobs' in data:
            jobs = data['openJobs']

            # Specify the CSV file and field names
            with open('jobs.csv', mode='w', newline='') as file:
                fieldnames = ['jobId', 'title', 'location', 'description', 'postedDate', 'employmentType', 'applyUrl']
                writer = csv.DictWriter(file, fieldnames=fieldnames)

                # Write headers and job rows
                writer.writeheader()
                for job in jobs:
                    writer.writerow({
                        'jobId': job['jobId'],
                        'title': job['title'],
                        'location': job['location'],
                        'description': job['description'],
                        'postedDate': job['postedDate'],
                        'employmentType': job['employmentType'],
                        'applyUrl': job['applyUrl']
                    })

            print("Jobs saved successfully to jobs.csv")
        else:
            print("No jobs found in the response.")
    except requests.exceptions.RequestException as error:
        print('Error:', error)

get_linkedin_jobs()

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Final Thoughts

Whether you have made up your mind to buy a database with the required information that is beneficial for your business or you want to invest in a web scraper from a third party, the choice is yours. Using any of the above options can help you save some precious time and money. On the contrary, having an in-house job scraper has its own perks. If done properly, you can get a job scraper within the same price range.

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