How to scrape business intelligence data from Michael Page using the Minexa.ai extension
- Minexa.ai
- 2 days ago
- 4 min read
Michael Page publishes hundreds of active job listings across London every day. If you want that data in a spreadsheet, structured and ready to analyse, the Minexa.ai Chrome extension gets you there without writing a single line of code.
This walkthrough covers the full extraction process from the michaelpage.co.uk/jobs/london page, showing exactly what you see at each step and what to do.
Watch the full tutorial first
The video below covers the complete workflow from install to export. Watch it through once before following the steps, so each screenshot makes immediate sense.
Now let's go through each step with screenshots.
Step 1: Open Minexa.ai and navigate to the page
Install the Minexa.ai Chrome extension if you have not already. Once installed, you will land on the Minexa home screen. From there, navigate directly to the Michael Page London jobs listing in your browser.
Once you are on the Michael Page London jobs page, you will see the full listing of active roles. This is the page Minexa will read and extract from.
Step 2: Confirm the detected page
Open the Minexa.ai extension popup. It will automatically detect that you are on a listing page and prompt you to confirm. Click the 'I'm on the right page' button to proceed. You do not need to point Minexa at anything manually.
After confirming, Minexa scans the page structure and detects how pagination works on this site, whether that is a next page button, infinite scroll, or a load more control.
Step 3: Review pagination detection
Minexa displays the pagination logic it found and asks you to confirm before continuing. This is where you verify it has correctly identified how to move through multiple pages of results. Click Continue when you are satisfied.
With pagination confirmed, Minexa knows it can follow through every page of London job listings automatically during the extraction run.
Step 4: Choose list only or list with detail pages
At this point you choose the depth of extraction. You can scrape just the listing data visible on the search results page, or instruct Minexa to follow each job link and pull the full detail from every individual job page as well. For a complete dataset including full job descriptions and requirements, select the list with linked details option.
Step 5: Select simple or advanced scraping mode
Simple mode handles most standard listing pages without any additional configuration. Advanced mode is available if you need custom interactions or a more specific workflow. For Michael Page London jobs, simple mode is sufficient.
Step 6: Confirm the data container
Minexa highlights the repeating container it has identified as the data source. This is the block of HTML that holds each job card. Review the highlighted area to confirm it matches the job listings on screen, then click to create the scraper.
Step 7: Review extracted data points
Once the scraper is created, Minexa shows you all the data points it found across the job cards. You can navigate through them using the next and previous controls to verify the fields before running the full job.
Fields you will see at this stage include job_role_description, job_description, job_summary, job_responsibilities, job_type, location, job_detail_url, job_ref, and the structured job_url array. That last field is worth understanding in detail.
The job_url field is not just a link. It returns a structured array per listing that encodes the job title anchor text, the relative href, the job card DOM id, the location, contract type, salary where available, the full description paragraph, bullet point responsibilities, the Save Job link with its ref attribute, and a View Job label. All of this comes from a single field, giving you a traversable object that mirrors the full card structure.
The job_title_description field captures two typed objects per listing: the clickable job title anchor and the Save Job action link. This lets you distinguish display text from interactive elements without any post-processing.
The job_id and job_id_attribute fields give you two identifier formats per listing. One is the DOM element id (for example job-9572841) and the other is the aria-style attribute value (for example jid-9572841). Both point to the same record and can be used for deduplication or to construct direct links to individual job pages.
Sample extracted data
Here is what a few rows from the extraction look like after the job runs:
[
{
"job_role_description": "Lead Data Engineer, Principle data engineer",
"job_description": "Lead Data Engineer",
"job_summary": "Opportunity to lead and shape a modern Azure-based enterprise data platform.",
"job_responsibilities": "Work with a global client base while driving impactful data and AI initiatives.",
"job_type": "Permanent",
"location": "London",
"job_detail_url": "/job-detail/lead-data-engineer-london/ref/jn-052026-7017918",
"job_ref": "9572841"
},
{
"job_role_description": "Machine Learning Engineer, AI Engineer, Deep Learning Engineer",
"job_description": "Machine Learning Engineer - London",
"job_summary": "Work on Cutting-Edge AI and Agentic Systems.",
"job_responsibilities": "Collaboration and Professional Growth",
"job_type": "Permanent",
"location": "London",
"job_detail_url": "/job-detail/machine-learning-engineer-london/ref/jn-042026-7007768",
"job_ref": "9499306"
}
]Each row maps directly to one job card on the page. The structure is consistent across all pages, so the same scraper works whether you are pulling twenty listings or two thousand.
Step 8: Run the job and export
After reviewing the data points, complete the configuration and run the scraping job. You can also connect a Google Sheet or set a schedule at this stage if you want the data refreshed automatically on a recurring basis.
Once the job is listed, hit the run button to start extraction across all pages.
Results appear in a table as pages are processed. When the run finishes, export to Excel, JSON, or Google Sheets directly from the results view.
Ready to collect structured job market data from Michael Page? Install the Minexa.ai extension and run your first extraction today.
If you are also interested in extracting jobs data from other platforms, this post on scraping jobs data from Apna using the Minexa.ai API covers a similar workflow from the developer side.
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