How to scrape jobs data from Naukri using Minexa.ai

Naukri lists thousands of H1 visa sponsorship roles across India, covering everything from immigration consulting to senior engineering positions. The data is public and updated frequently, but collecting it in a structured form manually is slow and hard to repeat. This walkthrough shows how to extract that data using the Minexa.ai Chrome extension, no code required.

Watch the tutorial first

The video below covers the full extraction from start to export. Watch it once before going through the steps.

Watch the tutorial at: https://www.youtube.com/watch?v=3zlwzCYDjrA

Step-by-step extraction walkthrough

Open Minexa.ai and install the Chrome extension if you have not already. Then navigate to the Naukri H1 visa sponsorship jobs page.

Once the Naukri listing page has loaded, open the Minexa extension from your browser toolbar. The extension detects the page automatically.

Click the 'I am on the right page' button to confirm the target page. This tells Minexa where to focus the scraper training.

Minexa then scans the page for pagination links. Review the detected pages and click Continue to proceed.

After pagination is validated, choose whether to scrape the listing page, detail pages, or both. For this use case, the list view captures all the fields needed.

Select simple or advanced scraping mode depending on how much control you want over the extraction. Simple mode works well for standard job listing pages like this one.

Click on the HTML container that wraps the full job listing block. Minexa highlights it in blue and begins identifying all data points inside it automatically.

Once the scraper is created, Minexa shows all extracted columns and the pagination it will follow across pages.

You can review the schedule options and set the job to run automatically at a chosen frequency if you want to track new listings over time.

Run the job from the scraping jobs list. Once it completes, the data appears in a structured table ready to export.

Export to Excel, JSON, or Google Sheets directly from the results view.

What the extracted data looks like

Here is a sample of two rows from the extraction, with field names cleaned up for readability:

[
 {
 "job_title": "Visa Filing Officer",
 "company": "West Highlander Immigration Consultancy Services Pvt. Ltd.",
 "rating": "1.1",
 "review_count": "2 Reviews",
 "location": "Chandigarh",
 "salary_or_location": "Chandigarh",
 "job_id": "010424501236",
 "job_link": "https://www.naukri.com/job-listings-visa-filing-officer-..."
 },
 {
 "job_title": "Senior Software Engineer",
 "company": "Earnin",
 "rating": "",
 "review_count": "",
 "location": "Bengaluru",
 "salary_or_location": "Bengaluru",
 "job_id": "300426502271",
 "job_link": "https://www.naukri.com/job-listings-senior-software-engineer-..."
 }
]

Key fields and what they tell you

Job title is extracted as a clean text string per listing, making it straightforward to filter or group roles by function.

Company rating and review count come from AmbitionBox and are bundled together in the raw output. The rating is a numeric score and the review count gives a sense of how much signal sits behind it. A rating of 4.2 backed by 200 reviews carries more weight than a 1.1 from 2 reviews.

Location is captured at sub-area granularity where available, for example Kochi(Kakkanad) or Noida(Sector 7), not just the city name. This makes geographic filtering more precise.

Salary or location surfaces a salary band when the listing includes one, such as 2-4 Lacs PA, and falls back to the city name when no salary is disclosed. Tracking this field across listings gives a quick read on which roles are transparent about compensation.

Job ID is a stable numeric identifier per listing. It is useful for deduplication when running the scraper across multiple sessions, since the same role will carry the same ID each time it appears.

Job link points directly to the individual listing detail page on Naukri, which can be used as input for a follow-up detail-page extraction to pull full job descriptions, required experience, and other fields not visible on the listing view.

Install the Minexa.ai extension

For related reading on building job data pipelines at scale, see how job data collection at scale actually works.