In October 2023, the number of job openings in the US was over 8.7 million. A few months earlier, there were over a million more job listings. It shows how quickly the market changes.
It's a dynamic market where employers and employees must be quick on their feet, staying informed about potential opportunities and acting accordingly.
But it can be difficult to keep track of all the data, especially because it changes over time. That's why job scraping has become a prevalent method for acquiring job data.
Today, we'll talk about scraping job postings, how this process works, the benefits, and how you can utilize it to your advantage.
What is job posting data?
Job posting data is the information you can find in job listings or advertisements employers publish to attract potential employees for their open positions. Depending on the platform where the job postings were published, it can include different data. Here's the job data you can expect to find:
Job titles & descriptions
Job postings data includes job titles and descriptions. These data points give a clear overview of the role that help understand what the job entails. The job description will also outline the duties, tasks, and responsibilities that come with the title.
Job qualifications and requirements
All job postings must outline the qualifications, experience, education, and skills required from the applications to be considered for the position. Potential applicants need to know whether or not they fit the criteria.
It helps job seekers apply for the right job postings, and companies get many quality applicants. For companies who analyze job posting data, it provides insights into what qualifications are popular, what tech stacks businesses use, and more.
Relevant company information
Company information is an essential aspect of job data. Some common company information in job posting data includes business location, industry, name, and some basic background.
Job salary and benefits
Job postings include the benefits employees can expect after working and the expected salary if accepted. That information builds trust and transparency while helping candidates understand if that role can fulfill their expectations.
Companies can analyze this data to get insights into competitive salaries in specific industries, popular/new benefits, and more. These insights can be helpful for crafting more competitive job offers or for simply analyzing the job market.
The benefits of job scraping
The main benefits of scraping job postings are:
Aggregating job listings
Web scraping job postings allows you to aggregate job listings from several platforms into a centralized location. It gives you a complete overview of available listings on multiple job boards.
Saves time
Extracting job postings can be an automated process, meaning that companies can quickly go through data from multiple job boards and save time.
Better job matching
Scraping job boards can be used to create sophisticated tools and algorithms for purposes like job matching, which enables job seekers to find positions they prefer or those that align with their skills.
Quick updates
Automated job board scraping means organizations can quickly gather data from multiple sources and get the latest information with regular updates.
Analyzing competition
Companies scrape job postings to analyze and monitor the job market with an overview of the required skills, salary trends, and job listings. It helps make informed decisions when it comes to acquiring the right talent.
Improved recruiting efforts
Job scraping allows for building tools that help optimize recruitment processes and build job funnels, helping recruiters fill positions with the right people more quickly.
Why is it hard to scrape job postings?
Even though job scraping has many benefits, there are also multiple challenges.
Getting accurate and quality data
Quality and accuracy are essential in web scraping. It's crucial to scrape job postings that are relevant and offer value. Websites often change structures, which could lead to errors during extraction. Multiple job board websites can have different listings that make it challenging to gather structured and quality data consistently.
Data duplicates
Scraping job data leads to duplicate entries, especially if the same job postings are present on multiple websites. Setting up systems and managing this duplicate data can be difficult for job scraping.
Dynamic job boards
Websites have various dynamic structures that load content using JavaScript. Typical scraping methods can have difficulties gathering dynamic job postings, which could lead to incomplete data.
Web scraping blocks
Only publicly available web data can be scraped. Still, many websites restrict the number of requests, block IP addresses if you're over the limit, and use anti-scraping mechanisms like Captcha and geo-blocking. Only a professional job scraper can go around these mechanisms.
Ethical and legal implications
Job scraping data professionals understand all of the terms of services of the websites they scrape. They understand the laws and regulations regarding scraping, and how to navigate through these regulations to ensure they're not harming anyone in the process.
How to scrape job postings?
Scraping is a process that can be done in different ways. It all depends on the job board, application, specific needs, and the type of scraper used. Here are some of the most used job scraping methods:
Manual job extraction
This is the simplest method for extracting job ads and tracking job trends. However, it's not scraping, it's a manual process where users go from one site to another to extract data. It's very time-consuming and leads to inconsistencies.
Web scraping and scripts
Developers create web scrapers and custom scripts for web scraping efforts. That includes extracting data, parsing it, and storing it in a desired format.
Job aggregator sites
Job aggregator sites use different web scraping methods to collect information from multiple job listing sites to give a wider picture of the actual market.
Scraping providers and APIs
Various third-party job scraping APIs and web scraping providers offer job extraction. They handle all aspects of scraping job portals and deliver the data in a structured format to their customers.
RSS feeds for collecting data
Some platforms give their users the option to subscribe to RSS feeds. These direct updates on the latest listings allow users to aggregate feeds from multiple platforms.
Job scraping alternatives
Collecting job data independently through web scraping can be technically complex, resource-intensive, and prone to compliance risks. Fortunately, there are several more efficient and reliable alternatives available:
1. Use job scraping as a service
Services like Bright Data, Oxylabs, and Apify offer job scraping infrastructure as a managed service. These job scrapers handle the technical and regulatory complexities of scraping, allowing you to focus on analyzing the data rather than collecting it.
2. Purchase pre-collected or one-time datasets
For businesses needing job data without ongoing updates, buying pre-aggregated datasets from data marketplaces such as Datarade can be a cost-effective solution. These are ideal for short-term research or proof-of-concept projects.
3. Source from specialized B2B data providers
Providers like Coresignal offer large-scale, continuously updated job posting datasets, sourced from multiple platforms and cleaned for enterprise use. This is the best option for organizations that need high-quality, up-to-date data delivered in a consistent format.
4. Use official job APIs
Some job boards and platforms offer their own APIs for accessing job listings (e.g., Indeed, LinkedIn, and other regional platforms). While often limited in scope or access, these APIs can be a reliable source for structured data if you need only a specific subset of job postings.
Buying jobs data vs scraping fresh job postings
Choosing between buying job data and scraping it yourself depends on your goals, technical capacity, and data needs. Scraping fresh job postings gives you control and flexibility but requires significant resources to maintain scraping infrastructure, ensure compliance, and manage data quality.
In contrast, purchasing job data from a trusted provider offers immediate access to clean, structured, and regularly updated records, which is ideal for organizations that prioritize speed, reliability, and scalability.
Conclusion
Web scraping job ads isn't easy, especially if you need large volumes of accurate, consistent, and quality data. That's why many organizations outsource these services to professionals who understand the ins and outs of web scraping applications.
