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A Complete Guide To Web Data Scraping Jobs For Beginners

Job Data Scraping

The biggest source of the data repository is the world wide web. There is an unimaginable volume of information on the internet that can be used along with data analytics, machine learning, and artificial intelligence for the growth of any business. Web data scraping refers to finding data and content from the internet. This data is then analyzed and manipulated for business needs. Job data scraping, in particular, refers to collecting job-related data. With more than 9.6 million job openings as of March 2023, the job data is crucial for hiring managers. 

Significance Of Big Data

Labor market intelligence (LMI) is critical for an organization’s recruiting process. It is useful for decision-making, evaluation, and policy design. This labor market analysis gives insight into the talent pool for specific job positions. It will also help recruiters determine the compensation package by checking salaries for similar job positions. Identifying the latest job market trends is critical to attracting top talent. The job market analysis is also useful in identifying skill gaps in the organization and determining the right course of action to plug the gap. So, large-scale real-time data is crucial for gathering insights into the job market.  

Applications Of Job Data Scraping

Job data scraping turns chaotic and unordered data into meaningful visualizations and insights. Organizations can use these insights to take crucial hiring decisions. Some of the applications of job data scraping for companies are:

  • Recruitment and hiring – Recruiters can analyze the job openings of competitors and identify talent needs. They can use job ads to see the latest market trends and types of jobs that get filled faster. This information can also identify potential candidates by understanding the type of jobs that generate better responses.
  • Market research and analysis – Companies must analyze the labor market to rethink their employment, recruitment, and hiring strategies. Labor market insights give valuable information to change their hiring process that helps attract and retain top talent. It is also useful in identifying the skills required for future projects. This market analysis helps businesses to become future-ready. 
  • Data-driven decision-making – Crucial recruitment and hiring decisions can be taken with data-driven insights using job data scraping tools. It will help companies determine the compensation package based on prevailing market rates. Leaders can customize their benefits offerings to attract top global talents. 

Challenges With Job Data Scraping

Job Data Scraping

Job data scraping requires you always to use real-time and new data. This process has to be repeated frequently to gather essential analytics for job market analysis. Some of the challenges in web scraping job data are:

  • Identifying the right sources to extract information 
  • Parsing a massive volume of unstructured data
  • Need for real-time data as the velocity of data consumption continue to increase
  • Data sources may be completely unstructured, semi-structured, or structured, making data extraction difficult
  • Bypassing anti-scraping techniques and tools employed in huge job portals to prevent crawling bots
  • Overcoming inconsistencies, duplication, abnormalities, or anomalies of big data
  • Huge cost of building and maintaining exclusive job data crawlers
  • Customizing the information to derive required data insights
  • Ensuring compliance while manipulating large volumes of data

Best Practices For Job Data Scraping

The websites are designed for human consumption. Data scraping using APIs, bots, and AI tools sends multiple requests to web pages to collect data. As a result, this process can drain the servers and utilize website resources, which is often frowned upon by website owners. So, following ethical practices for job data scraping is crucial to ensure compliance and avoid breaking the law. 

Pay Attention To Robots.Txt

Every website has a robots.txt file that contains all the information necessary for crawling bots. Sometimes, access to certain pages may be blocked. In that case, it is unethical to scrape the website. 

Never Overload Servers

Before crawling websites, you must know about the acceptable number of requests that can be sent to the server. Causing server failure is disruptive to end users.

Avoid Peak Hours

Website crawling takes its toll on the servers, and you must avoid doing it during peak hours. Non-peak hours scraping will increase the bot speed. 

Prefer Headless Browser

Headless browsers load only the HTML portions that are sufficient to scrape. It saves resources and time for scraping. 

Honey Pot Traps

Websites are designed to prevent crawler bots from hoarding the servers. Some links are designed as honey pots that a human user will never click. When bots access those links, you may permanently be banned from that site. 

Tools For Job Data Scraping

For web scraping of job data, you need big data analysts and data scientists. The job data is largely unstructured, as there is no standard format. Building a resourceful database requires parsing, managing, and analyzing job data after data extraction. The sources for this job data can be career pages of companies, large job boards, job aggregator websites, niche job portals, etc. Different types of job data parsed include job title, description, location, requirements, and compensation. Some of the common ways of job data scraping are:

  • In-house team – Only large enterprises have the resources to set up an in-house scraping team to scrape large-scale job data. This job requires high technical skills; for market analysis, job data scraping must be done frequently. Apart from people, large servers running crawling scripts, a streamlined extraction-transfer-storage process, and business analytic tools to gather insights are important. 
  • Hiring a web scraping service – Hiring a third-party web scraping service provider is easier because the data will be presented to you in the exact format you want. However, this is expensive, and the quality of the data you get is not always trustworthy. 
  • Web scraping tool – An automated web scraping tool now enables you to fetch web data with specific parameters. Even people without technical skills can use such tools extensively. When an AI-powered tool is used, it can be set up to automate the entire market research process so that you get real-time data delivered in the format you want. 


The best technique for job data scraping is to use an AI-powered scraping tool. It can be accessed as frequently as you want, and you will get access to real-time and updated data. The automated job feeds provide data you can analyze to derive business insights. These tools can also be customized to feed the data to an analytics engine to create market metrics.  

JobsPikr offers cloud-based job data scraping tools that can be customized for your business needs. You can integrate JobsPikr with your enterprise system in just 60 minutes. The tool’s capabilities can also be extended to give you direct business insights. 

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