Naukri Scraper: 21 Data Fields, Up to 500 Free Results/Month (2026)

Each record carries 23 output fields, including title, company, location, experience, salary, and skills, from Naukri.com, India's largest job board. It offers flexible input options: either paste a direct Naukri Search URL to parse its filters automatically, or use structured input controls for keyword, location, experienceYears, salaryLakhs, jobAge, jobType, workMode, and sortBy. You can try it before paying, as Apify's free plan includes $5.00 of monthly usage, covering up to 500 results of this Actor. This is ideal for recruiters building talent pipelines or HR professionals conducting market analysis, but it's not for anyone who needs to scrape applicant data or recruiter contact details, which are not included in the output.

Try it before you read further. Apify's free plan includes $5.00 of usage every month with no credit card, enough for up to 500 results at $0.01 each before platform usage. Open Naukri Scraper on Apify and run the prefilled example.

How reliable is Naukri Scraper in production?

Across the last 30 days of public runs on the Apify platform, Naukri Scraper recorded 65 runs with the following outcomes.

No run failed or timed out in the last 30 days; the 1 that did not finish was stopped by the people who started them. Keep a retry and an alert on scheduled runs all the same: a clean month is a record, not a guarantee.

What does it cost to run Naukri Scraper?

Each result costs $0.01 on Apify's free plan, which is $10.00 per 1,000 results. Starting a run is charged separately at $0.005 per GB of Actor memory. Apify also bills the platform usage each run consumes, at the rates of your Apify plan, on top of these charges.

Worked example: collecting 10,000 results costs $100.00 in result charges before run-start fees and platform usage. No run failed or timed out in the last 30 days, so the list price is a fair budget; keep a retry in place all the same.

The primary driver of cost is the maxItems input control, which directly limits the number of job listings returned. The simplest way to evaluate whether this Actor meets your needs without incurring significant charges is to keep the maxItems value low, such as the default of 50. Apify's free plan allows for experimentation, providing $5.00 of monthly usage to cover up to 500 results, excluding platform usage.

How do you run Naukri Scraper from the API?

None of its 10 controls is strictly required, so the defaults below produce a valid run on their own. The payload below uses the schema's own prefilled values, so it runs as written once you substitute your API token.

Call the synchronous endpoint to start a run and receive dataset items in one request:

curl -X POST "https://api.apify.com/v2/acts/crawlerbros~naukri-scraper/run-sync-get-dataset-items?token=$APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"searchUrl":"https://www.naukri.com/python-developer-jobs-in-bangalore","maxItems":5,"sortBy":"relevance"}'

The same run from Python, using the official client:

from apify_client import ApifyClient

client = ApifyClient("<YOUR_APIFY_TOKEN>")

run_input = {
  "searchUrl": "https://www.naukri.com/python-developer-jobs-in-bangalore",
  "maxItems": 5,
  "sortBy": "relevance"
}

run = client.actor("crawlerbros~naukri-scraper").call(run_input=run_input)

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item)

And from Node.js:

import { ApifyClient } from 'apify-client'

const client = new ApifyClient({ token: '<YOUR_APIFY_TOKEN>' })

const input = {
  "searchUrl": "https://www.naukri.com/python-developer-jobs-in-bangalore",
  "maxItems": 5,
  "sortBy": "relevance"
}

const run = await client.actor('crawlerbros~naukri-scraper').call(input)
const { items } = await client.dataset(run.defaultDatasetId).listItems()
console.log(items)

The synchronous endpoint holds the connection open until the run finishes, which is convenient for small batches and wrong for large ones. For anything long running, start the run asynchronously and poll, or attach a webhook, so a dropped connection does not cost you the results.

Which Naukri Scraper inputs matter, and which can you skip?

This Actor exposes 10 input controls. For a quick start, provide a searchUrl from Naukri and set a low maxItems value. Most users can leave sortBy at its default "relevance" and experiment with specific filters like keyword and location only after a successful initial run.

• searchUrl (string): A Naukri.com search URL to scrape. Paste any URL from https://www.naukri.com - the scraper parses the slug and filters automatically. If empty, the structured filter fields below are used instead.
• maxItems (integer): Maximum number of job listings to return. Each Naukri API page returns up to 20 jobs. Default: 50.
• keyword (string): Job title or keyword (e.g., "python developer", "data scientist"). Used only when Search URL is empty.
• location (string): City name (e.g., "bangalore", "delhi-ncr", "mumbai", "hyderabad"). Used only when Search URL is empty.
• experienceYears (integer): Years of experience required.
• salaryLakhs (string): Annual salary range in lakhs.
• jobAge (string): Only include jobs posted within the last N days.
• jobType (string): Employment type filter.
• workMode (string): Filter by remote or hybrid work. Naukri does not have an explicit 'office' filter - office is the default state of every listing, so leave this empty for office jobs.
• sortBy (string): How results are ordered. Default: "relevance".

Fixed-choice controls: salaryLakhs accepts 0-3 (0 - 3 Lakhs), 3-6 (3 - 6 Lakhs), 6-10 (6 - 10 Lakhs), 10-15 (10 - 15 Lakhs), 15-25 (15 - 25 Lakhs), 25+ (25+ Lakhs); jobAge accepts 1 (Last 1 day), 3 (Last 3 days), 7 (Last 7 days), 15 (Last 15 days), 30 (Last 30 days); jobType accepts full-time, part-time, internship, contractual; workMode accepts remote (work from home), hybrid; sortBy accepts relevance, date (newest first), salary (highest first).

What does Naukri Scraper return?

Notably, the output omits any personal contact information for recruiters or applicants, focusing solely on the job posting details. Empty fields are omitted to maintain clean, non-null records.

• id: Naukri job ID
• title: Job title
• url: Full Naukri job listing URL
• companyName: Company name
• companyId: Naukri company ID
• companyRating: AmbitionBox rating (out of 5)
• companyReviewCount: Number of reviews
• location: Job location (may list multiple cities)
• experience: Required experience range
• salary: Salary range as displayed
• description: Job description snippet
• skills: Array of skill tags
• industry: Industry
• role: Job role category
• jobTypeFlags: Flags such as premium, easy_apply, walk_in
• postedDate: When the job was posted
• logoUrl: Company logo URL
• vacancies: Number of vacancies
• applyCount: How many people applied
• recruiterName: Recruiter name
• scrapedAt: UTC timestamp when scraped

These are the documented fields. Optional ones can be empty on a given record, so measure how often each field your deliverable depends on is populated across a real sample before automating the handoff.

How do you build the workflow end to end?

Open Naukri Scraper and work through these in order. Each step ends with something to check, so a bad configuration surfaces on a small run rather than a scheduled one.

1. Set searchUrl to a specific Naukri.com search URL (e.g., for "python developer" jobs in "bangalore") and initially set maxItems to 5 for a quick test.
2. Execute the Actor and verify in the log that it successfully initialized and processed the given URL.
3. Inspect the dataset preview to confirm that job records are returned and that key fields such as title, companyName, and url are populated as expected.
4. To broaden your search, clear the searchUrl field and instead use the structured input controls like keyword, location, and experienceYears, then run again.
5. Adjust maxItems to your desired output volume, mindful that each Naukri API page returns up to 20 jobs.
6. Review a larger sample of the output to ensure data consistency, particularly for fields like salary and skills, and check for any unexpected empty records.
7. Schedule the Actor to run periodically, implementing monitoring for any sudden drops in the number of returned results or changes in the structure of critical fields.

How do you apply it? Three worked playbooks

These are Naukri Scraper's own documented use cases, each worked through as an operating pattern rather than a description.

Use case 1: Recruitment agencies

Outcome: Recruitment agencies: build talent pipelines and track competitor openings

Configure: To identify specific talent, set keyword to a relevant job title like "java developer" and location to a major city such as "hyderabad". Set jobAge to "7" days for fresh listings.

Working method: Start with narrow searches for high-demand roles in key regions, then expand to broader keywords or additional locations. Compare the collected data against known hiring trends.

Deliverable: A curated list of recent job postings, including title, companyName, skills, and postedDate, organized by role and target location for talent outreach.

Stop condition: If the quantity of new job postings for a defined keyword or location drops significantly (e.g., by more than 20%), or if critical skill fields are empty.

Use case 2: Job seekers

Outcome: Job seekers: automate job discovery matching your skills

Configure: To find matching jobs, set keyword to your primary skill, such as "data scientist". Specify your experienceYears (e.g., 3-5) and preferred salaryLakhs range (e.g., "6-10").

Working method: Run the Actor with your core criteria. Refine your search by adding or changing location and jobType. Prioritize jobs with a recent postedDate and review skills for alignment with your profile.

Deliverable: A daily or weekly summary of new job listings matching your skills, experience, and salary expectations, delivered via email or an internal tool.

Stop condition: When the output frequently includes irrelevant job types (e.g., non-tech roles in a tech search) or if crucial filters like salaryLakhs are not reflected in the results.

Use case 3: HR analytics

Outcome: HR analytics: salary benchmarking and market demand analysis

Configure: For a broad market view, set location to target cities like "bangalore" and leave keyword empty. Use sortBy: "salary" to focus on high-paying roles.

Working method: Conduct broad searches across target locations and industries. Analyze salary and experience trends. Examine the distribution of job types and required skills to identify market demands.

Deliverable: A comprehensive report or dashboard detailing average salaries by role and region, prevalent in-demand skills, and the market share of remote or hybrid positions.

Stop condition: If salary or experience fields show a high rate of nulls or inconsistent formatting, or if location data is too ambiguous for precise regional analysis.

What breaks, and how do you design around it?

• Over the last 30 days, 0.0% of public runs failed and 0.0% timed out. Build retries and alerting around those rates rather than assuming every run completes.

This Actor returns up to 800 jobs per run. To get more data, split your search by location, keyword, or salary range, running multiple times with narrower inputs. If your output needs to span many months, schedule recurring runs and process the unique job IDs to avoid duplication.

When should you not use Naukri Scraper?

This Actor is best suited for current job market data from Naukri.com. If your goal is to analyze broader global hiring trends, a different approach may be more effective. For example, if you need data from multiple job boards beyond India, consider using job board scrapers such as PowerToFly Jobs Scraper or Jobbio Jobs Scraper. Similarly, if your primary need is salary data and company culture insights rather than job postings, the PayScale Salary & Company Culture Scraper would be a more direct fit, as it is designed to extract detailed compensation and review information. Do not use this Actor if you require contact information for applicants or recruiters, as it does not provide such details; its focus is on public job listing content.

What should you check before trusting the output?

• Verify that the skills array contains actual skill tags, not empty strings or malformed entries; alert if this field is consistently malformed.
• Check that the salary field, when present, contains a consistent format (e.g., "8-15 Lacs PA"); flag any entries that deviate or are unexpectedly null.
• Confirm that postedDate is recent and accurately reflects when the job was listed; stale dates could indicate a data freshness issue.
• For roles where companyRating and companyReviewCount are crucial, ensure these fields are populated and within expected ranges; a high rate of nulls suggests a parsing problem.
• Ensure the description snippet is sufficiently detailed and not unexpectedly short or truncated, which might indicate an incomplete record.
• Monitor for sudden drops in the count of returned job listings for a given query, as this can signal a blocking issue or a change in Naukri's site structure.

None of this proves a record is correct. It gives a scheduled Naukri Scraper run defined points where it should stop instead of quietly passing bad data downstream.

Frequently asked questions

What is the typical success rate for this Actor?

In the last 30 days, this Actor recorded 65 public runs, with 64 succeeding (98.5%). There were no failures or timeouts, indicating strong operational reliability when scheduling unattended runs. One run was aborted by a user, which typically means it was stopped manually before completion, not a system failure.

How can I control the cost of running this Actor?

The cost primarily depends on the number of results returned, which is controlled by the maxItems input. Each result costs $0.01 on Apify's free plan. A first run using the example input caps maxItems at 5, costing up to $0.05 in result charges. Remember that platform usage is billed separately, on top of result charges.

Does this Actor require any special proxy configuration?

No, you do not need to configure a proxy. An India RESIDENTIAL proxy is hardcoded and applied automatically because Naukri's Akamai bot protection blocks Apify datacenter IPs, so the scraper combines patchright stealth Chromium with RESIDENTIAL/IN traffic. No configuration is needed from you. Sessions are rotated automatically (up to 5 attempts) on transient blocks.

What should I do if the job listings contain missing or incomplete data?

Empty fields are omitted from records to keep the dataset clean. If you find critical fields consistently missing, check your input parameters to ensure they are specific enough. For example, if salary data is important, ensure you are not searching for roles where salary is rarely disclosed.

Can I filter job listings by specific criteria like experience or salary range?

Yes, this Actor supports filtering by several structured fields. You can specify a keyword, location, experienceYears (0-30 years), salaryLakhs (e.g., "10-15" lakhs), jobAge, jobType, and workMode (remote/hybrid). Alternatively, you can paste a Naukri.com search URL, and the Actor will automatically parse its embedded filters, making it easy to replicate complex searches.

Where to go next

When you are ready to run it, open Naukri Scraper on Apify; the free plan covers up to 500 results a month.

Start with the Naukri Scraper Actor page for the current input schema, pricing tier, and run history.

If you are comparing approaches rather than committing to one Actor, these category pages list every option we publish:

• Job listing scrapers covers 81 Actors in this family.

Other Actors we maintain for related data:

• PowerToFly Jobs Scraper: Scrape diversity-focused tech job listings from PowerToFly - search by keyword, filter by employment type, remote status, and experience level.
• Jobbio Jobs Scraper: Scrape global job listings from Jobbio - search by keyword, filter by contract type and experience level, paginate through thousands of openings.
• NoFluffJobs Remote Tech Jobs Scraper: Scrape NoFluffJobs.com - a transparent IT job board with 3,800+ active remote tech listings.
• ZipRecruiter Jobs Scraper Pro: Extract job postings from ZipRecruiter.com with FULL job descriptions.
• BrighterMonday Scraper: Scrape live job listings from BrighterMonday.co.ke, Kenya's largest job board.
• Bayt.com Middle East Jobs Scraper: Scrape Middle East job listings from Bayt.com with titles, companies, locations, salaries, career level, experience, full descriptions, skills, and apply links.
• Glassdoor Jobs Scraper: Scrape job listings from Glassdoor by keyword, location, or company URL.
• PayScale Salary & Company Culture Scraper: Scrape PayScale.com salary research pages: company compensation & culture ratings, employee reviews, and job-title salary data by company, city, gender, degree, and experience.

Related guides:

• ZipRecruiter Jobs Scraper Pro: 3 Practical Use Cases
• Glassdoor Jobs Scraper: Three Operating Patterns and Configurations
• The Hub Startup Jobs Scraper: 19 Data Fields per Record (2026)
• CvOnline Job Scraper: 3 Practical Use Cases

Resources

• Actor documentation, input schema, and pricing: verified against the published Actor on 2026-09-25.

• Actor last updated by its maintainers on 2026-04-15.

• Run outcome figures cover the 30 day public window ending 2026-09-25.

• Naukri Scraper on Apify