Naukri.com Jobs Search Scraper: Extract India's Largest Job Portal Data

Understanding Naukri.com and Its Market Dominance

Naukri.com dominates India's online recruitment landscape as the country's largest job portal, operated by Info Edge. With over 75 million registered job seekers and 100,000+ active employers, it serves as the primary hiring platform for Indian IT, finance, healthcare, and manufacturing sectors.

The platform specializes in Indian market nuances: salary ranges in INR/LPA (Lakhs Per Annum), work-from-home filtering, company ratings via Ambition Box integration, and skill-based matching for India's tech-heavy job market. For recruitment agencies targeting Indian talent, market researchers analyzing IT hiring trends, or job aggregators building comprehensive databases, Naukri data provides unmatched insights into Asia's third-largest economy.

This scraper automates extraction from search result pages, transforming filtered job listings into structured datasets ready for analysis, integration, or competitive intelligence.

What This Scraper Extracts and Who Should Use It

The Naukri.com Jobs Search Scraper processes search result pages, capturing multiple job listings efficiently. Unlike detail page scrapers requiring individual URLs, this tool handles entire search pages with filters applied.

Key extracted data: Job titles, company names and IDs, logos, salaries (currency and detail), experience requirements (min/max years), skills and tags, job descriptions, application deadlines, creation dates, URLs, work modes (office/hybrid/remote), Ambition Box ratings, and saved job status.

Target users:

IT Recruitment Agencies track tech hiring trends across programming languages, frameworks, and experience levels. Job Aggregators build comprehensive Indian job databases. Market Researchers analyze salary trends, skill demand, and hiring velocity in India's tech sector. Companies benchmark compensation, monitor competitor hiring, and identify talent shortages. Career Platforms integrate Naukri data for enhanced job discovery.

Input Format

The Actor supports two scraping modes — use only one at a time:

1. Scrape by URLs — scrape data directly from a list of Naukri job search result URLs. Example Configuration:

{
  "urls": [
    "https://www.naukri.com/full-stack-developer-jobs-2?wfhType=2"
  ],
  "ignore_url_failures": true,
  "max_items_per_url": 20
}

2. Scrape by search filters — scrape data using a keyword and additional filters (location, work type, experience, salary, publish date, etc.) instead of providing URLs. Example Configuration:

{
    "keyword": "python",
    "location": "india",
    "work_type": "3",
    "experience": "5",
    "salary": "3to6",
    "published_at": "30",
    "page": 1,
    "max_items_per_url": 100,
}

There is also a General settings group that applies to both modes.

Example Screenshot:

1. Scrape by URLs

Use this mode when you already have specific Naukri search result URLs. When this mode is used, all options in the "Scrape by search filters" section will be disabled.

Field	Type	Default	Description
urls	array	(required)	One or more Naukri job search result URLs to scrape. You can paste URLs one by one, or use the Bulk edit section to add a prepared list. The example shows full-stack developer jobs with the work-from-home filter (wfhType=2). Build URLs by performing a search on Naukri, then copying the resulting URL with all filters intact.
ignore_url_failures	boolean	true	If true, the scraper continues running when a URL fails instead of stopping the entire run. Essential when scraping multiple searches / batch processing.

URL Building Tips: Naukri encodes filters as query parameters — ?k=python (keyword), &experience=5 (years), &cityType=metropolitan (location tier), &wfhType=2 (work from home). Build multiple URLs to cover different job titles, locations, or experience bands.

2. Scrape by search filters

Use this mode when you want to search using a keyword and additional filter attributes instead of scraping by URLs. If you use this mode, leave the urls field above empty.

Field	Type	Default	Description
keyword	string	—	The keyword to search for.
location	string	—	The location to search for (city, department, postal code).
work_type	string ("", 0, 1, 2, 3)	—	Filter by work type: Any (empty), Work from office (0), Temp (1), Remote (2), Hybrid (3).
experience	string	—	Filter by years of experience. Valid values: 0–30 (number of years).
salary	string ("", 0to3, 3to6, 6to10, 10to15, 15to25, 25to50, 50to75, 75to100, 100to500)	—	Filter by minimum salary band, e.g. 0-3 Lakhs, 3-6 Lakhs, up to 100-500 Cr. Select Any (empty) for no filter.
published_at	string ("", 1, 3, 7, 15, 30)	—	Filter by publish date: Any (empty), 1 day, 3 days, 7 days, 15 days, 30 days.
page	integer	—	The page number to start scraping from.

3. General settings

Applies to both scraping modes above.

Field	Type	Default	Description
proxy.useApifyProxy	boolean	false	Set false if Naukri doesn't require proxies for your use case, or true (with residential proxies) for enterprise-scale scraping. Test without proxies first — Naukri is generally accessible without them.
max_items_per_url	integer	20	The maximum number of job listings to collect per URL/search. Naukri typically shows 20 results per page — increase to 50–100 for more comprehensive extraction across multiple result pages.

Complete Output Structure and Field Definitions

Title: Job position name (e.g., "Senior Full Stack Developer"). Primary categorization field.

Logo Path / Logo Path V3: Company logo URLs. V3 is newer format. Use for displaying jobs visually.

Job ID: Unique Naukri identifier for the posting. Primary key for databases, deduplication.

Currency: Salary currency (typically "INR"). Essential for multi-market analysis.

Footer Placeholder Label / Color: UI elements for job card display. Indicates job freshness or special status.

Company Name: Hiring organization. Links to employer profiles, tracking hiring activity.

Is Saved / Saved: Boolean indicating if job was bookmarked (reflects scraper account state, not universally useful).

Tags and Skills: Array of required/preferred skills (e.g., ["React", "Node.js", "MongoDB"]). Critical for skill demand analysis and candidate matching.

Placeholders: Additional job metadata or UI indicators. Context-dependent display elements.

Company ID: Unique identifier for employer. Links multiple jobs from same company.

Job Description URL: Direct link to full job posting. Access complete details beyond search snippets.

Static URL: Alternative/canonical job URL format. Backup linking option.

Ambition Box Data: Company ratings and reviews from Ambition Box (Info Edge property). Employer reputation insights—ratings, review counts, culture scores.

Job Description: Brief excerpt visible in search results. Quick context for filtering/analysis.

Show Multiple Apply: Flag indicating if job has multiple application routes. Application complexity indicator.

Group ID / Is Top Group: Job grouping identifiers (similar positions from same employer). Deduplication and volume hiring signals.

Created Date: When job was posted. Freshness indicator, posting velocity analysis.

Mode: Employment mode—"office," "hybrid," "work from home." Critical for remote work trend analysis.

Board: Job category or vertical (IT, Finance, Sales). High-level classification.

Salary Detail: Structured salary object with min/max ranges. Typically in LPA (Lakhs Per Annum) for Indian market.

Experience Text: Human-readable experience requirement (e.g., "3-5 years").

Minimum Experience / Maximum Experience: Numeric experience bounds in years. Precise filtering and analysis.

Apply By Time: Application deadline timestamp. Urgency indicator, time-to-fill tracking.

Sample Output:

[
  {
  "title": "Full stack  Developer",
  "logo_path": "https://img.naukimg.com/logo_images/groups/v1/40604.gif",
  "logo_path_v3": "https://img.naukimg.com/logo_images/groups/v1/40604.gif",
  "job_id": "091225912848",
  "currency": "INR",
  "footer_placeholder_label": "6 Days Ago",
  "footer_placeholder_color": "grey",
  "company_name": "Siemens",
  "is_saved": false,
  "tags_and_skills": "full stack,rest,test automation,full stack developer,testing framework,sql,spring framework,software architecture",
  "placeholders": [
    {
      "type": "experience",
      "label": "4-6 Yrs"
    },
    {
      "type": "salary",
      "label": "Not disclosed"
    },
    {
      "type": "location",
      "label": "Bengaluru"
    }
  ],
  "company_id": 73816,
  "jd_url": null,
  "static_url": "siemens-jobs-careers-775",
  "ambition_box_data": {
    "url": "https://www.ambitionbox.com/reviews/siemens-india-reviews?utm_campaign=srp_ratings&utm_medium=desktop&utm_source=naukri",
    "reviews_count": 5616,
    "aggregate_rating": "3.9",
    "title": "Siemens Reviews by Employees"
  },
  "job_description": "Experience: 4-6 years <br><br>Experience working with Git /Bit-bucket. .",
  "show_multiple_apply": false,
  "group_id": 40604,
  "is_top_group": 0,
  "created_date": 1765258936698,
  "mode": "jp",
  "board": "1",
  "salary_detail": {
    "minimum_salary": 0,
    "maximum_salary": 0,
    "currency": "INR",
    "hide_salary": true,
    "variable_percentage": 0.0
  },
  "experience_text": "4-6 Yrs",
  "minimum_experience": "4",
  "maximum_experience": "6",
  "apply_by_time": "3:12 PM",
  "saved": false,
  "from_url": "https://www.naukri.com/full-stack-developer-jobs?src=popular_roles_homepage_srch"
}
]

Step-by-Step Usage Guide

1. Define Target Jobs: Identify job types, skills, locations, or experience levels you need. Test searches on Naukri.com to refine filters.

2. Build Search URLs: Copy URLs from test searches. For large datasets, create multiple URLs: different technologies ("python-jobs," "java-jobs"), experience bands (0-2 years, 3-5 years), locations (Bangalore, Mumbai, Hyderabad).

3. Configure Input: Add URLs to JSON config. Set max_items_per_url to 20 for standard pages or higher for deep extraction. Enable ignore_url_failures for robustness.

4. Execute Scrape: Launch via Apify. Processing 5-10 search pages (100-200 jobs) typically completes in 2-4 minutes.

5. Export Data: Choose JSON for databases, CSV for analysis. Clean by removing duplicates using job_id or filtering by created_date for recent postings only.

6. Handle Pagination: For jobs spanning multiple pages, either include page URLs manually (...&page=2, ...&page=3) or set max_items_per_url higher to auto-paginate.

Strategic Applications for Indian Job Market Intelligence

Tech Skill Demand Tracking: Analyze tags_and_skills across thousands of jobs to identify trending technologies. Track React vs. Angular adoption, cloud platform preferences (AWS/Azure/GCP), or emerging frameworks.

Salary Benchmarking: Salary_detail data enables compensation analysis by skill, experience, location, and company size. Calculate average LPA for specific tech stacks or roles.

Remote Work Trends: Mode field tracks work-from-home vs. office vs. hybrid distribution. Analyze which industries/roles offer remote options most frequently.

Hiring Velocity Analysis: Created_date patterns reveal hiring surges (seasonal, funding-driven) and company expansion phases. Track posting frequency by employer.

Experience Level Demand: Minimum/maximum_experience fields show market demand distribution—entry-level saturation vs. senior talent shortages.

Company Hiring Monitoring: Track specific companies via company_id and company_name. Monitor competitors' job types, volumes, and growth signals.

Application Urgency Insights: Apply_by_time reveals hiring urgency. Short windows (7-14 days) indicate desperate hiring; long windows (60+ days) suggest selective processes.

Maximizing Data Value and Best Practices

Weekly Scraping Cadence: India's tech market moves fast. Weekly scrapes capture new postings and market shifts. Store historical data for trend analysis.

Segment by Technology: Create separate searches for Python, Java, JavaScript, Cloud, DevOps, Data Science. Cleaner datasets, easier analysis.

Enrich with Ambition Box: Use ambition_box_data to correlate company ratings with hiring difficulty (low-rated companies may struggle to fill roles).

Geographic Segmentation: India's metro cities (Bangalore, Pune, Hyderabad, Mumbai, NCR) have distinct markets. Scrape separately for regional insights.

Quality Checks: Flag anomalies—salaries outside normal ranges, missing experience requirements, or very old created_dates may indicate data issues.

Skill Co-occurrence Analysis: Mine tags_and_skills to find skill combinations (React + Node.js + AWS). Helps identify full-stack requirements and technology ecosystems.

Respect Rate Limits: Space out large scraping runs. Sustainable practices ensure continued access.

Conclusion

The Naukri.com Jobs Search Scraper unlocks India's largest job market dataset. From tech skill trends to salary benchmarks and remote work adoption, this tool transforms public job data into actionable intelligence for recruitment, market research, and competitive analysis in Asia's fastest-growing digital economy.