Scrape Job Listings Efficiently in 2024 with NetNut Proxies

Introduction

The world has become a global village for those with internet access. Therefore, various activities, including job hunting, can be easily carried out online. The trend of hiring and job-hunting online became more prominent, especially after the pandemic.  

In addition, employees and hiring managers need access to timely data to remain competitive and stay informed about potential opportunities. Majority of job searches occur online through job boards, Google searches, and job platforms like Upwork, Fiverr, and Indeed. 

Although online job searches have become a prominent and effective means of job hunting, they can also be quite challenging. Therefore, this guide will examine how to scrape job data, the common challenges, and how to optimize the process with NetNut proxies.

How to Obtain Job Posting Data?

There are several options that you can explore if you have decided to scrape job posting data, and they include the following:

Building an in-house scraper

One of the alternatives includes building an in-house job scraper. This process can accumulate to several thousand dollars, especially if you don’t have a developmental and data analysis team. On the other hand, you would not be reliant on any third party tool for access to updated and real-time job data.

The advantage of building an in-house scraper is that you have full control over your scraping infrastructure. In addition, it offers higher flexibility as you can tailor it to meet a specific web scraping need. However, building and maintaining an in-house scraper is resource-intensive.

Buying a pre-built scraper

This alternative involves purchasing a ready-made tool. Therefore, it eliminates the cost of funding the development team as well as maintenance. However, you are largely relying on the expertise of another person. This can be seen as a disadvantage because you have less control over the scraping tool. The benefits of these options include scalability, and it is less expensive in terms of development and maintenance costs.

Purchasing job database

This may be the easiest way to obtain data from job posting listings. All you need to do is buy pre-scraped job datasets from data companies that specialize in data scraping. Therefore, buying a job database is easy to use and does not require resources for the developmental process. On the other hand, you have no control over how the data is collected. In addition, there is a chance that you are buying outdated information.

Benefits of Learning How To Scrape Job Postings 

Here are some benefits of learning how to scrape job posting data:

Market research 

Collecting data from job posting listings allows job market analysts and businesses to gather information for market research. This data can provide insight into the skills which are in high demand. In addition, it provides a comprehensive understanding of the job market and current trends. Therefore, this data can be useful to job seekers in aligning their skills with the trends.

Optimize job hunting

Another benefit of scraping job posting data is that it optimizes job hunting. In other words, it helps job seekers search job listings from various sources to find opportunities that match their educational qualifications and skills. As a result, they can be matched with jobs that are a top candidate, and this increases their chances of landing their dream jobs.

In addition, scraping job postings allows job seekers to identify how much companies are willing to pay for a particular position. Arming yourself with this information helps you to tailor your applications and set realistic expectations during negotiations. Since job postings usually include information about salary and other benefits, you can understand the current trend.

Optimizing recruitment process 

Hiring managers can facilitate the hiring process by extracting data from job posting listings. This data provides insight into the benefits and minimum requirements for specific roles. 

In addition, businesses can scrape job postings to build a talent pool, which involves identifying potential candidates that may be a great fit at the company. Scraping job posting data allows businesses to build systems that facilitate the recruitment process so the hiring team can quickly find the right people for the position. 

Furthermore, job posting data provides valuable insights into recruitment trends including the skills in demand, average time it takes to fill a position, and the most effective channels for posting jobs. Subsequently, this information plays a significant role in optimizing your recruitment strategy.

Competitor analysis 

Another benefit of scraping job posting data is competitor analysis. It allows businesses to analyze and monitor how competitors are listing positions, skills, and benefits for a specific role. Therefore, it helps to make informed decisions when hiring the best candidate for a position. In addition, competitors’ job analysis reveals their hiring strategies and the kind of talent they want which you can leverage for improving your business strategy. Subsequently, this information can become an invaluable tool that allows businesses to benchmark their hiring practices and stay competitive.

Lead generation 

Lead generation is another benefit of scraping job posting data. Job seekers can make a list of companies hiring within their preferred location. They can generate leads depending on the type of job -remote , on-site, or hybrid. The best thing about this is it can be done from your computer without physically going from one company to another. 

In addition, scraping job postings allows you to aggregate job openings from several platforms in one location. As a result, it gives job seekers a broad overview of job opportunities and access to various job boards.

How to scrape Job Postings With Python in 2024

As discussed earlier, there are various methods to collect job posting data. However, in this section, we will focus on how to build a job scraper with Python- one of the best programming languages for web scraping. Python has a simple syntax, powerful web scraping libraries, extensive documentation, and an active community.

Step 1: Prepare the environment

The first step in building your job scraper is to set up the coding environment. Since we are using Python, you need to download and install the latest version from the official website. In addition, you need to download an integrated development environment- it provides a space to write, compile, and debug the code to promote productivity. The most popular IDEs used with Python are Visual Studio Code and PyCharm.

Now that you have installed the preliminary software, you can proceed to install the request library via pip (the Python package installer) on your terminal:

python -m pip install requests

Moving on, you need to create a new Python file to store all your code. Once that is created, the next step is to import the Python libraries that you need to create a job scraper, as shown below:

import requests, json, csv

These libraries are crucial regarding how to scrape job postings in 2024. The requests library is responsible for sending HTTP requests to the API. On the other hand, the json and csv libraries are necessary to properly store the data after extraction and parsing.

Step 2: Claim your free trial

Go to NetNut’s website to claim your 7-day free trial when you register for an account. Once you create an account, you will be redirected to an expert who will guide you in choosing the best proxy solution for your needs. After creating your API username and password, store them in a variable like this:

API_credentials = (‘USERNAME’, ‘PASSWORD’)

Step 3: Create the API payload

Before proceeding to create the API payload, define the target URL – the website you want to scrape its job postings data; for example, let us use the Bettercloud jobs URL. Then, in your Python file, create a payload dictionary that will contain all the scraping and parsing instructions that the API requires.

payload = {

    ‘source’: ‘universal’,

    ‘url’: ‘https://www.bettercloud.com/job-board/’,

    ‘geo_location’: ‘United States’,

}

In the code above, the geo_location parameter is used to instruct the API to use a proxy server within the United States. Depending on your preferred location, as indicated in the code, you can use any location worldwide.

Load more listings

BetterCloud, by default, loads about 9 job listings on each page. However, when you select ‘Load more,” it loads an additional 15 job listings. Subsequently, you can simulate button clicks by using the API’s Headless Browser. Now, you can add an instruction to the payload and multiply it by 12 to scrape about 180 job postings:

payload = {

    ‘source’: ‘universal’,

    ‘url’: https://www.bettercloud.com/jobs’,

    ‘geo_location’: ‘United States’,

    ‘render’: ‘html’,

    ‘browser_instructions’: [

        {

            ‘type’: ‘click’,

            ‘selector’: {

                ‘type’: ‘xpath’,

                ‘value’: ‘//button[contains(text(), “Load more”)]’

            }

        },

        {‘type’: ‘wait’, ‘wait_time_s’: 2}

    ] * 13

}

In addition, you can optimize the code by increasing the repetition count to load more listings.

Fetch a resource

An alternative to writing selectors for each data point is to fetch all the data from a JSON-formatted resource. Subsequently, this makes scraping job posting lists easier as you can access more data points beyond what is seen in the HTML file, such as IDs or geolocation data.  

Therefore, you can access the resource by visiting the target URL and opening the Developer Tools. You can use the following shortcuts to open the Developer Tools depending on your device operating system. 

For Windows: F12 or Ctrl +Shift + I

For macOS: Command + Option + I

Once you open it, click on the Network tab and filter for Fetch/XHR resources, then find the first instance of a resource that begins with the query?x-algolia-agent=Algolia. The next step is to open the resource’s Response tab to view the job postings in JSON format.

Open the resource’s Headers tab to find the request URL. Then, you can instruct the API to access this resource by defining the fetch_resource function and indicating a regular expression pattern that finds a matching URL. Therefore, you can use the lookahead assertion to match the last occurrence of the resource that starts with a query?x-algolia-agent=Algolia and contains all the loaded job data. 

The payload code is shown below:

payload = {

    ‘source’: ‘universal’,

    ‘url’: ‘https://www.bettercloud.com/job-board’,

    ‘geo_location’: ‘United States’,

    ‘render’: ‘html’,

    ‘browser_instructions’: [

        {

            ‘type’: ‘click’,

            ‘selector’: {

                ‘type’: ‘xpath’,

                ‘value’: ‘//button[contains(text(), “Load more”)]’

            }

        },

        {‘type’: ‘wait’, ‘wait_time_s’: 2}

    ] * 13 + [

        {

            “type”: “fetch_resource”,

            “filter”: “^(?=.*https://km8652f2eg-dsn.algolia.net/1/indexes/Jobs_production/query).*”

        }

    ]

}

Step 4: Send a request to the API

The next step involves creating a response object that sends a POST request to the API. Don’t forget the API credentials that are required for authentication and for passing the payload as a JSON object. 

The code is shown below:

response = requests.request(

    ‘POST’,

    ‘https://serp-api.netnut.io/queries’,

    auth=API_credentials,

    json=payload,

    timeout=180

)

results = response.json()[‘results’][0][‘content’]

print(results)

data = json.loads(results)

After running the above code, the AP returns the response. Subsequently, you need to parse the response by accessing the scraped content from the results > content keys. Afterwards, you need to use the json module to load the data properly as a Python dictionary.

Step 5: Parse JSON response

At this step, you need to create an empty jobs list and parse the JSON results to extract only the data you need. For this guide, you can use the .get ( ) function as shown below:

jobs = []

for job in data[‘hits’]:

    parsed_job = {

        ‘Title’: job.get(‘title’, ”),

        ‘Location’: job.get(‘location’, ”),

        ‘Remote’: job.get(‘remote’, ”),

        ‘Company name’: job.get(‘company_name’, ”),

        ‘Company website’: job.get(‘company_website’, ”),

        ‘Verified’: job.get(‘company_verified’, ”),

        ‘Apply URL’: job.get(‘apply_url’, ”)

    }

    jobs.append(parsed_job)

Note that you can provide more instructions if you want to retrieve other data points like geolocation.

Step 6:Save the scraped data into a CSV

To aid the readability status of the scraped data, you need to parse the results to a CSV file. Therefore, you can use the built-in csv_module to get it done:

fieldnames = [key for key in jobs[0].keys()]

with open(‘betterclouds_jobs.csv’, ‘w’) as f:

    writer = csv.DictWriter(f, fieldnames=fieldnames)

    writer.writeheader()

    for item in jobs:

        writer.writerow(item)

Challenges Associated with Scraping Job Postings in 2024

Scraping job postings can present several challenges, and they include:

Anti-scraping measures 

Data scraping has become a trend in recent years. Therefore many websites have implemented strategies to ensure the safety of their data. Some of these anti-scraping measures include CAPTCHAs, which is a test designed to tell humans apart from computers. Therefore when the target website detects an unusual traffic from your IP address, it can trigger IP blocking to prevent scraping. 

Dynamic websites 

Modern websites have dynamic content which means they are heavily dependent on JavaScript for loading content. This poses a challenge for web scraping as regular scrapers may not be equipped to handle dynamic elements. As a result, the scarper will return incomplete data which defeats the purpose of building a scraper.

Data Quality 

Another challenge to scraping job postings is data quality and consistency. Since job postings come in various formats and structures, it becomes quite a challenge for scrapers to extract relevant information consistently across multiple sources. 

Getting accurate and quality data is a significant aspect of scraping job postings. Subsequently It is crucial to ensure that data collected is relevant. Since different job boards have different listings and structure, getting structured and quality data can be a challenge. 

Legal and ethical issues

The legality of web scraping varies according to your state laws and the website’s terms of service. Therefore, scraping job posting may cause legal issues if the website’s terms of service strictly prohibit scraping. Usually, scraping publicly available data is fair, but it becomes a legal issue when the data is categorized as private or sensitive information. Therefore it becomes necessary to understand these regulations so your scraping is within ethical boundaries .

Best Practices for Scraping Job posting

The first tip you must not overlook is understand the rules and ethics of web data extraction. You can follow these practices to ensure ethical practices:

• Avoid overloading the server with too much requests
• Go through the robots.txt file
• Check the website’s terms of service
• Use obtained data ethically and give credit to the original source as needed.

The second tip is to use reputable proxies. CAPTCHAs, honeypot traps, and IP ban are common anti-scraping measures that you don’t have to worry about when using NetNut proxies. 

Choosing The Best Proxy For Job Scraping- NetNut

One of the best practices for effective job scraping is the use of proxies. NetNut is an industry leading proxy provider that offers multiple proxy solutions that guarantees 99.9% uptime to ensure uninterrupted data access. 

NetNut rotating residential proxies are your automated IP rotating solution that ensures you can access job listings despite geographic restrictions. These proxies have high confidence ratings because they are associated with actual addresses.

Datacenter proxies offer high speed for optimized job posting data retrieval. Although they are less expensive than residential proxies, they may be easy to identify by servers with advanced technology. 

Alternatively, you can use NetNut’s Mobile Proxy to access various job boards without fear of IP bans and CAPTCHAs.

Conclusion

This guide has examined the significance of scraping job posting as well as how to build a web scraper. Choosing the right tool for scraping job posting plays a significant role in the outcome. With NetNut Scraper API, you have access to real time data without restrictions associated with anti-scraping measures.

If you choose to build a scraper from scratch, you need to get premium proxies. NetNut offers various proxy solutions that mask your actual IP address to prevent IP bans so you can enjoy access to unlimited data. 

Contact us today to get started!  

Frequently Asked Questions

What are the things to consider before building a job scraping tool?

Here are some things that you need to do before building a job scraper:

• Define the programming language, APIs, and web scraping frameworks that are most suitable to your use case. In addition, make choices that you are comfortable using so you don’t get stuck half-way.
• Set up a stable testing environment that can handle the challenges associated with building a scraper. 
• Invest in data storage, so you have adequate facilities to store the data you worked really hard to retrieve.

What is job scraping?

Job scraping is the process of collecting job posting data from websites like LinkedIn, Indeed, and many others. However, this process requires the use of bots to automate web data retrieval for efficiency. Some of the details you can get from scraping job posting data include:

• Job title
• Company name
• Location of the company
• Salary and other benefits
• The date the job was posted

What are the methods of scraping job data?

Various methods can be used for scraping depending on the job board, specific use case, and type of scraper used. Some of the methods include:

• Manual job scraping: This is the simplest method of collecting job listing data. However, it is not suitable for large-scale processes. It involves visiting a job board and manually copying the data you need. Subsequently, it is error-prone, time-intensive and effort-intensive if you need access to a large volume of data.
• Web scraping scripts: You can leverage programming languages to write a script that can scrape job listing data. 
• Web scraping APIs: Various third-party APIs offer job extraction. They extract the data from the target site and deliver it in a structured format to the customers.