How to scrape Naukri job postings for the India market

What fields does a Naukri job scraper return per posting?

Every Naukri row uses the same flat schema as the other boards, then adds columns that only exist because Naukri exposes them. The standard part covers id, title, company, location, job_url, a job_url_direct apply link when the posting has one, date_posted, job_type, is_remote, and the full description in Markdown or HTML. Company context comes as company_rating, company_url, company_logo, and company_description where Naukri shows them.

The Naukri-only part is the reason to scrape this board rather than rely on a global aggregator. skills holds the skills extracted from the posting, experience_range holds the years of experience the employer asks for, vacancy_count holds the number of open positions, and work_from_home_type holds Naukri's own hybrid, remote, or office label. Salary arrives as salary_min, salary_max, salary_currency, and salary_interval, with salary_source telling you whether the figures came from structured data or were parsed out of the description text. Each row also records matched_search_term and a scraped_at timestamp, so multi-term sweeps and repeat runs stay traceable.

• skills: the skills extracted from the posting, populated on Naukri and no other board
• experience_range: the years of experience Naukri lists as required, low end to high end
• vacancy_count: open positions on the posting, a direct hiring-intensity signal
• work_from_home_type: Naukri's WFH category, separate from the generic is_remote flag
• company_rating: Naukri's employer score, carried on the same row as company
• salary_min, salary_max, salary_currency, salary_interval, and salary_source when a range is posted

What is different about scraping Naukri compared with Indeed or LinkedIn?

Naukri is one of the boards the scraper reads through a real browser rather than a plain HTTP request, which is why the README puts it at 1 to 3 minutes per run while Indeed and LinkedIn land in 5 to 20 seconds. Rows stream in as each board finishes, so a mixed run shows faster boards first.

Coverage is India only. The countryIndeed input does nothing for Naukri itself; it only matters if you add Indeed or Glassdoor to the same run, in which case set it to india so those boards return Indian inventory too. Naukri postings quote pay in rupees, so check salary_currency on each row before comparing against USD rows from other boards. enforceAnnualSalary converts monthly figures to yearly equivalents.

maxResults caps at 100 per board per search term, searchTerms takes up to 5 queries, and offset paginates past the first page. Skills and experience are output columns, not input filters, so pull a wide set and filter after the run.

• Browser-fetched: expect 1 to 3 minutes, versus 5 to 20 seconds for Indeed and LinkedIn
• India coverage only; countryIndeed applies to Indeed and Glassdoor, not Naukri
• salary_currency separates rupee ranges from USD rows in a multi-board dataset
• enforceAnnualSalary normalizes monthly pay to a yearly figure
• Up to 100 rows per search term per run, 5 terms per run, offset for pagination
• Filter on skills and experience_range after the run; they are not search inputs

How do recruiters and researchers use Naukri skills and experience data?

The skills column turns a job search into a demand signal. Run the same 5 search terms every morning with hoursOld set to 24, count how often each skill appears across the fresh rows, and you have a daily view of which technologies Indian employers are asking for, broken down by city through the location field. experience_range adds a seniority axis without parsing description text: group rows by the minimum years requested and you can see whether a market is hiring juniors or leads.

vacancy_count is the field sales and staffing teams watch. A posting with 15 open positions is a different lead from a single opening, and because company and company_rating sit on the same row, you can rank hiring companies by volume and by how their employees rate them. For salary research, salary_min and salary_max with salary_source set to direct_data give you employer-stated ranges you can trust more than figures parsed from prose. Export as CSV or Excel to pivot these columns, or push rows to Google Sheets or a CRM through Zapier, Make, or n8n.

• Daily skill-demand tracking: hoursOld 24, count skills per run, split by location
• Seniority mix from experience_range without reading descriptions
• vacancy_count ranks companies by how many positions they are filling
• company_rating next to company lets you qualify employers on the same row
• salary_source direct_data marks employer-stated ranges for compensation studies
• CSV, Excel, JSON, XML, or RSS export, plus Zapier, Make, and n8n connectors

How do you call the Naukri scraper from code or an AI agent?

From code, POST your input to https://api.apify.com/v2/acts/openclawai~job-board-scraper/runs with your Apify token and read the default dataset when the run finishes, or use the run-sync-get-dataset-items endpoint to get the rows back in one call. In Python, install apify-client and call client.actor("openclawai/job-board-scraper").call(run_input={"searchTerm": "DevOps engineer", "sites": ["naukri"], "maxResults": 30}), then list items from the dataset it returns. Node uses the same shape through apify-client on npm.

For agents, add https://mcp.apify.com/?tools=fetch-actor-details,openclawai/job-board-scraper to Claude, Cursor, or any MCP client, and the model can ask for Naukri postings and reason over the structured rows without touching HTML. Schedule the run in Apify Console, attach a webhook, and each finished run triggers your downstream processing. Naukri is one of 8 boards the same actor covers, and the setup guide for Claude Desktop and Cursor lives at datapika.com/actors/job-board-scraper.

• REST: POST to acts/openclawai~job-board-scraper/runs, then read the default dataset
• One-call option: run-sync-get-dataset-items returns the rows directly
• Python and Node: apify-client with sites set to ["naukri"]
• MCP: mcp.apify.com tool URL for Claude, ChatGPT, and Cursor
• Schedules and webhooks in Apify Console for a daily India feed

Questions, answered

Keep reading