The job board industry has seen tremendous growth and change in recent years, and the opportunities for scraping job listings data are more compelling than ever in 2024. With companies continuing to invest heavily in talent acquisition and job seekers flocking online, whoever can aggregate the most high-quality job listings has the potential to own a highly lucrative market.
In this comprehensive guide, we‘ll walk through everything you need to know to build a successful job board website by leveraging web scraping to source listings from the career sites of the Fortune 500. We‘ll cover the different approaches to obtaining job listing data at scale, provide a detailed tutorial for scraping any company‘s careers page using modern tools, and reveal key insights from LinkedIn‘s meteoric rise that you can apply to fuel your own job board‘s growth.
Whether you‘re a entrepreneur looking to enter the space or an established player seeking to expand your listings footprint, read on to learn cutting-edge strategies and tactics for scraping the Fortune 500 and dominating the job board market in 2024 and beyond.
The State of the Job Board Industry in 2024
Before diving into the technical details of web scraping, it‘s essential to understand the current lay of the land in the job board industry. Despite the entrance of massive players like Google into the space in recent years, the market for job listings remains highly fragmented with ample room for new entrants to carve out defensible niches.
A few key trends have shaped the evolution of the industry leading into 2024:
- Continued growth in recruitment marketing spend as companies compete aggressively for talent, driving increased demand for effective, targeted job boards and listings distribution channels
- The mainstream adoption of remote work vastly expanding the geographic scope of job searches for both employers and candidates
- The maturation of programmatic ad buying making it easier than ever for job boards to efficiently monetize their listings inventory and user data
- Rapid advancements in AI-powered matching and recommendation engines to deliver more relevant listings to job seekers
With the right approach, these trends create a incredibly favorable environment for building a high-growth job board business today. At the core of any successful job board is high-quality, comprehensive, and fresh database of job listings, which is where web scraping comes in.
Deciding What to Scrape: Company Career Sites vs. Job Search Engines
There are two primary sources for obtaining job listings data via web scraping: directly from the careers sections of individual company websites or from large job search engines and aggregators like Indeed, ZipRecruiter, CareerBuilder, etc. Each approach has its own unique advantages and trade-offs to consider.
The main benefits of scraping listings directly from company websites include:
- Getting listings data straight from the source, ensuring maximum freshness and accuracy
- Gaining access to listings that may not be syndicated to the major job boards
- Having full control over your listings inventory and not being dependent on third-party platforms
The downsides are that you need to build and maintain scrapers for every individual company site you want to cover, which can require significant ongoing development resources. You also miss out on the "long tail" of listings from smaller companies that don‘t have the brand recognition of the Fortune 500 but that can provide valuable liquidity for your marketplace.
Scraping from job search engines, on the other hand, offers the major advantage of acquiring a huge volume of listings spanning many employers with a single scraper. You can efficiently build a large database covering not just the largest enterprises but many SMBs as well. The trade-off is potentially reduced data freshness vs. direct scraping (since jobs may remain listed on aggregators after being taken down from company sites) and less differentiation vs. competing job boards.
In practice, the most effective approach is often to use a combination of both, scraping the career sites of Fortune 500 employers for differentiated, high-accuracy listings while also pulling in supplementary listings from the major aggregators. Next, we‘ll walk through the exact steps to scrape listings from any company website using cutting-edge scraping tools.
Step-by-Step Guide to Scraping Company Careers Pages (with Examples)
While you can technically use custom scripts to scrape job listings from company websites, the development and maintenance costs tend to be prohibitively high due to the need to account for each site‘s unique structure and continuously adjust to frontend changes. Instead, the most efficient and economical approach for most scraping use cases is to leverage a visual web scraping tool like Octoparse or ParseHub.
For this example, we‘ll use Octoparse to scrape job listings from Facebook‘s career site. The same general process can be adapted for any Fortune 500 company with minimal adjustments.
Step 1: Navigate to Facebook‘s Careers Page
Go to Facebook‘s main careers page at https://www.facebook.com/careers/. As of 2024, the URL structure has changed a bit with listings now being surfaced under https://www.facebook.com/careers/jobs. We‘ll use this updated URL for our example.
Step 2: Create a New Octoparse Task & Visual Sitemap
Open up Octoparse and create a new task. Using the Octoparse browser, navigate to the Facebook jobs URL above. Octoparse will automatically generate a visual sitemap of the target page with each page element clearly labeled.
Step 3: Configure Pagination
On the Facebook careers page, we can see that the listings are paginated, with a "Load More Jobs" button at the bottom used to load additional listings. To ensure we capture all available listings, we need to instruct Octoparse to load all of the listing pages.
Octoparse makes this easy with a "Pagination" widget found under the "Actions" panel of the sitemap designer. Simply select the "Load More Jobs" button and choose "Loop click on each element" under the Pagination options. This will instruct the scraper to keep clicking the button until no more listings are loaded.
Step 4: Identify Target Data Fields
Next, we need to specify which data points we want to capture from each job listing. On the Facebook listing page, the key fields we‘ll extract are:
- Job Title
- Location
- Job Category
- Job Posting Date
To capture these, simply select each target element in the visual sitemap and choose "Extract text" under the "Action" panel.
Step 5: Crawl Detail Pages
For additional context on each job, we‘ll also want to capture the full description, responsibilities, requirements, and any other details available on the dedicated listing page for each job. To do this, we need to instruct Octoparse to crawl the detail page linked from each listing on the results page.
In the visual sitemap, choose the listing title, which links to the detail page URL, and select "Open links in new tabs." On the resulting detail page visual sitemap, select the elements you want to capture (e.g. description, responsibilities, etc.) and choose "Extract text or HTML."
Step 6: Run Crawl & Export Data
With the visual sitemap complete, you‘re ready to start your crawl. Choose "Start Extraction" in the top-right corner of the Octoparse dashboard. The crawler will automatically navigate the Facebook careers sitemap, walking through all of the listings and detail pages and capturing the specified data fields.
Once complete, you can export your scraped job listings data in CSV, JSON, or a variety of other structured formats, ready to be ingested into your job board platform.
Using this same basic process with Octoparse or other visual scraping tools, you can quickly scale up your scraping efforts to cover any number of Fortune 500 career sites, building a robust database of the freshest, highest-quality job listings to power your board.
Driving Differentiation Through Niche Targeting & Data Analysis
Simply aggregating a large volume of job listings isn‘t enough to build a sustainable competitive advantage in the job board space. To truly stand out and win the loyalty of employers and job seekers, you need to drive meaningful differentiation through thoughtful curation and packaging of your listings inventory.
One highly effective approach is to zero in on a specific niche within the broader job market and then build the definitive job board for that vertical. Rather than compete head-on with the likes of Indeed or LinkedIn, carve out a focused segment to dominate.
A good example of this in practice is Dice, a hugely successful tech-focused job board that has built a dominant brand within IT/engineering by deeply understanding the needs of tech employers and candidates. By tailoring their platform, partnerships, and marketing to a specific persona, Dice has managed to thrive despite competing for listings and users with far larger generalist sites.
To identify the most promising niches to target with your job board, start by deeply analyzing your scraped listings data to surface insights into the supply and demand dynamics of different roles, industries and locations. By enriching your raw listings data with additional context like company size/industry, role seniority, salary ranges, location, etc., you can spot the pockets of opportunity where there is high demand from employers but limited supply of highly relevant, targeted job boards.
As an example, let‘s say you scraped 10,000 data science job listings from across the Fortune 500 and found that demand (as measured by listing volume) was heavily concentrated in a handful of emerging tech hubs like Austin, Denver, and Raleigh. You could use this insight to create the go-to job board for data science roles in those geographies, building specific features and content to cater to that audience and forming partnerships with local university programs and data science communities to drive engagement.
Lessons from LinkedIn‘s Playbook
As you build out your job board platform, there‘s no better model to learn from than LinkedIn, which has executed arguably the most successful playbook in the industry over the past two decades. By deeply understanding how LinkedIn has approached the space, you can adapt many of the same underlying strategies and tactics to your own business.
A few of the key drivers behind LinkedIn‘s massive growth and defensibility:
Leveraging their existing professional social network to drive liquidity to their jobs marketplace. The more users they acquired for their core professional networking product, the more attractive the platform became to both job seekers and employers.
Building rich, structured profile data that allows highly granular targeting of candidates by recruiters, making their listings inventory more valuable than basic job postings on other sites.
Investing heavily in a content ecosystem (via the LinkedIn Feed, Pulse, Groups, Learning, etc.) to keep users engaged on a daily basis vs. only visiting when actively searching for a job.
Forming deep enterprise relationships to become the system of record for all hiring workflows (via LinkedIn Recruiter) vs. just a listings site.
While you may not have the same scale and resources as LinkedIn, you can still put their key strategies to work for your job board business. For example:
- Build sticky features beyond just job search to keep users coming back (e.g. a community forum for your niche, original blog content, email newsletters, etc.)
- Capture rich structured data on both job seekers and employers to power better matching and targeting.
- Go deep on a specific persona and build the definitive end-to-end workflow for their hiring needs vs. just a transactional listings site.
The more indispensable you can make your platform to the employers and candidates in your niche, the better positioned you‘ll be to fend off competitors and drive sustainable growth.
The Future of Job Boards & Web Scraping: 2024 & Beyond
As you build your job board business, it‘s critical to stay on top of the latest trends and technologies shaping the space to avoid being left behind. In the web scraping world, 2024 has brought a wave of new advancements that are making it easier than ever to obtain high-quality, real-time job listings data at scale.
On the crawling side, the latest generation of headless browser tools like Puppeteer and Playwright have made it far simpler to automate scraping of client-side rendered sites and single-page apps. Many web scraping services (e.g. ScrapingBee, ScrapingBot) now offer headless browsers as a service, eliminating the need to manage this infrastructure yourself.
For extraction, AI-based OCR (optical character recognition) and NLP (natural language processing) techniques have become far more accessible and accurate, making it possible to automatically pull structured data fields out of job descriptions and other unstructured text content.
Looking ahead, the use of machine learning and graph-based approaches to power "identity stitching" across disparate job listings is set to be the next major frontier in job board data quality. By training models to identify and merge duplicate listings across sites, you‘ll be able to present job seekers with the most comprehensive and non-redundant view of available opportunities.
By staying on the cutting edge of these web scraping innovations, you‘ll be able to rapidly scale your job board‘s listings acquisition efforts while driving major efficiency gains vs. legacy scraping approaches. Combined with strong brand positioning, robust engagement features, and an obsessive focus on your target niche, high-quality web scraped listings data will be your secret weapon for winning the job board wars in 2024 and beyond.