Naukri Job Scraper

Naukri Job Scraper collects job listings from Naukri.com and turns them into structured data for hiring research, salary benchmarking, and competitor monitoring. Provide a job title or keyword with an Indian city, or paste Naukri search or company URLs, then export title, company, location, experience, salary, and work mode to JSON, CSV, or Excel. Use it to build job databases, track hiring, and schedule recurring collection. Pricing starts at $3.00 per 1,000 jobs, with optional job details and company overviews at the same rate and AmbitionBox reviews at $2.00 per 1,000.

What can you automate with Naukri Job Scraper?

• Build targeted job databases — Search titles or skills such as "python developer" across Indian cities, or nationwide, and export company, experience, salary, and work mode on every row.
• Benchmark pay and experience — Compare salary bands and year ranges by role and city for compensation research.
• Monitor competitor hiring — Filter by freshness and sort by date to see who is hiring, for which titles, and how recent the listings are.
• Collect one employer's live jobs — Paste a Naukri or AmbitionBox company page and pull that company's openings up to your job cap.
• Attach employee sentiment — Optionally nest AmbitionBox reviews on each job from the same company.
• Schedule recurring collection — Run daily or weekly and push new listings into Sheets, a CRM, or an alert workflow.

Who is this scraper for?

Team	Workflow
Recruitment and staffing agencies	Build city-and-title pipelines of open roles and watch which employers are hiring this week.
Compensation and HR analytics teams	Compare experience bands, disclosed salaries, and work modes across Indian markets.
Talent-intelligence researchers	Combine live jobs with company size, headquarters, followers, and AmbitionBox reviews.
Sales and lead-generation teams	Spot companies hiring for a skill, then enrich those accounts downstream.

What data can you collect from Naukri?

Data group	Example fields	How it helps
Job identity	title, jobId, url, postedDate, postedAgo	Identify each listing and deduplicate across runs with jobId.
Employer	companyName, companyUrl, rating	See who is hiring and how the company is rated.
Role terms	location, experience, salary, workMode, employmentType	Filter by city, years, pay, and office / hybrid / remote.
Company overview	companySize, headquarters, followCount	Qualify employer scale when Scrape Company Overview is enabled.
Employee reviews	reviews (rating, likes, dislikes, work policy)	Read sentiment on the same job row when Scrape Company Reviews is enabled.

Listing-card fields are always collected. Full description, skills, education, apply/view counts, and employmentType appear when Scrape Job Details is enabled. Overview fields are charged once per unique company and copied onto that company's job rows. Reviews nest on those job rows — they are not separate dataset items. Salary and some overview fields are empty when Naukri does not publish them.

How to use Naukri Job Scraper

1. Open the Naukri Job Scraper on Apify.
2. Enter a search query such as "python developer" and an optional location such as "bangalore", or paste Naukri search / company URLs.
3. Set Max Jobs (Console prefill is 20; 0 means unlimited) and any sort, freshness, or work-mode filters.
4. Run the Actor — it writes one dataset row per job.
5. Export the dataset or connect it to your workflow.

{
    "searchQueries": ["python developer"],
    "locations": ["bangalore"],
    "maxJobs": 20,
    "sortBy": "relevance",
    "freshness": "7",
    "workMode": "any",
    "scrapeJobDetails": false,
    "scrapeCompanyOverview": false,
    "scrapeCompanyReviews": false,
    "proxyConfiguration": {
        "useApifyProxy": true,
        "apifyProxyGroups": ["RESIDENTIAL"]
    }
}

Example workflow

Build a weekly list of Python developer jobs in Bangalore

1. Schedule a Monday run with searchQueries = ["python developer"], locations = ["bangalore"], freshness = "7", and sortBy = "date".
2. Keep rows that match your experience band and work-mode needs using the experience and workMode fields.
3. On shortlisted companyUrls, enable Scrape Company Overview and Scrape Company Reviews when you need size, HQ, and employee sentiment.
4. Send new records to Google Sheets or a CRM through a webhook after each run.
5. Deduplicate user-side against the stable jobId field.

The Actor does not filter or merge results across runs — those steps happen in your pipeline.

Automate and integrate your results

• Schedules — Run daily with freshness = "1" for new-job alerts, or weekly with freshness = "7" for market snapshots. Create a separate scheduled task per query and city.
• Webhooks — Push completed runs into Google Sheets, Make, Zapier, Slack, a CRM, or an ATS.
• Exports and API — Download JSON, CSV, Excel, or HTML from the Apify Console, or start a run from the API:

curl -X POST "https://api.apify.com/v2/acts/scraptivo~naukri-scraper/runs?token=YOUR-APIFY-TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "searchQueries": ["python developer"],
    "locations": ["bangalore"],
    "maxJobs": 20,
    "freshness": "7",
    "proxyConfiguration": {
      "useApifyProxy": true,
      "apifyProxyGroups": ["RESIDENTIAL"]
    }
  }'

• Deduplication — Within a run, listings are deduplicated by jobId. Across runs the Actor appends new results. Use jobId as the stable merge key.

Input reference

Field	Type	Required	Default	What it controls
searchQueries	Array of strings	No*	[]	Job titles, skills, or keywords (e.g. "python developer"). Combined with each location.
locations	Array of strings	No	[]	Indian cities as shown on Naukri (e.g. "bangalore", "hyderabad", "pune", "remote"). Empty = nationwide.
startUrls	Array of URLs	No*	[]	Naukri search pages or job-listing URLs. Used in addition to search queries.
companyUrls	Array of URLs	No*	[]	Naukri company pages or AmbitionBox review pages. Live jobs collected up to Max Jobs.
maxJobs	Integer	No	0	Maximum job rows (0 = unlimited). Console prefill is 20. Overview fields and reviews attach onto these rows.
sortBy	String	No	relevance	Search sort: relevance or date. Applies to keyword / location searches.
freshness	String	No	any	Only jobs posted within any, 1, 3, 7, 15, or 30 days. Keyword / location searches only.
workMode	String	No	any	any, office, hybrid, or remote. Keyword / location searches only.
scrapeJobDetails	Boolean	No	false	Fetch full description, skills, education, apply/view counts, and employmentType. Charged as job-details.
scrapeCompanyOverview	Boolean	No	false	Attach company profile fields onto each job from that company. Charged as company-overview once per unique company.
scrapeCompanyReviews	Boolean	No	false	Attach AmbitionBox reviews as a reviews list on each job. Charged as company-reviews per review.
maxReviewsPerCompany	Integer	No	20	Maximum AmbitionBox reviews per company (0 = unlimited). Ignored when reviews are off.
detailConcurrency	Integer	No	5	Max parallel job-detail / company requests (1–20).
proxyConfiguration	Proxy object	No	Apify proxy, RESIDENTIAL	Proxy settings. Residential proxies are recommended.

* Provide at least one of searchQueries, startUrls, or companyUrls.

Which source should I use?

Use searchQueries plus locations for keyword discovery — every query is searched in every location, and sortBy, freshness, and workMode apply. Use startUrls when you already have a Naukri search or listing URL; those URLs are scraped in addition to keyword searches. Use companyUrls for one employer's live jobs from a Naukri overview page or an AmbitionBox reviews page.

Do overview and reviews create extra dataset rows?

No. Max Jobs is a row cap of one job per row. Overview fields and the reviews array nest onto each job from the same company. You still pay the overview event once per unique company and the review event per review, but you never get unjoined enrichment rows.

Output example

Each dataset item is one job. With company overview and reviews enabled, a record looks like this:

{
    "jobId": "150126031660",
    "title": "Python Developer (@Infosys)",
    "companyName": "Infosys",
    "url": "https://www.naukri.com/job-listings-python-developer-infosys-infosys-kolkata-hyderabad-bengaluru-5-to-10-years-150126031660",
    "companyUrl": "https://www.naukri.com/infosys-overview-13832",
    "location": "Kolkata, Hyderabad, Bengaluru",
    "experience": "5-10 Yrs",
    "salary": "Not disclosed",
    "workMode": "Hybrid",
    "postedAgo": "7 Days Ago",
    "postedDate": "2026-08-11T10:39:09.094000+00:00",
    "rating": 3.5,
    "companySize": "100001+",
    "headquarters": "Bengaluru",
    "followCount": 1878902,
    "sourceQuery": "python developer",
    "reviews": [
        {
            "reviewId": 78544194,
            "title": "rated by a Technical Lead in Bengaluru",
            "rating": 1.0,
            "likes": "Culture and env.",
            "dislikes": "Manager are no supportive...",
            "employmentType": "permanent",
            "workPolicy": "Hybrid"
        }
    ]
}

employmentType on the job itself is filled when Scrape Job Details is enabled. Listing-only runs still return title, company, location, experience, salary, work mode, posted date, URL, and job ID when Naukri publishes them.

How much does it cost to scrape Naukri?

The Actor uses pay-per-event pricing. Each run charges a small Actor Start event at $0.00005. Then you pay only for the events you trigger:

• $3.00 / 1,000 jobs — a dataset-item event for every job row pushed to the dataset.
• $3.00 / 1,000 job details — a job-details event per job when Scrape Job Details is enabled.
• $3.00 / 1,000 company overviews — a company-overview event once per unique company when Scrape Company Overview is enabled.
• $2.00 / 1,000 reviews — a company-reviews event per AmbitionBox review when Scrape Company Reviews is enabled.

A listing-only run of 20 jobs costs about $0.06 plus the start event. A listing-only run of 1,000 jobs costs $3.00. Adding job details on those 1,000 rows costs $6.00 in event fees. If the same 1,000 jobs come from 200 unique companies and you enable overviews, add $0.60. One thousand reviews add $2.00.

Apify paid plans discount the per-1,000 rates: Bronze $2.90 / Silver $2.80 / Gold $2.70 for jobs, job details, and company overviews; reviews are Bronze $1.90 / Silver $1.80 / Gold $1.70. Compute units and residential proxy usage are billed separately on your Apify plan. When your event spending limit is reached, the run stops.

Reliability and responsible use

• Proxy requirement — The Actor defaults to the Apify proxy with the RESIDENTIAL group. Residential IPs are recommended; raise detailConcurrency only while that group is enabled.
• Retries and concurrency — Failed requests are retried automatically. detailConcurrency defaults to 5 (maximum 20). Listing-card data is still returned when a detail request fails.
• Conditional fields — Job-detail fields, overview fields, and the reviews array appear only when the matching toggle is enabled. Naukri also leaves salary and some profile fields unset.
• Search spelling — Location values should match Naukri's search spelling (for example bangalore, not Bengaluru). Result rows may still display "Bengaluru".
• Public data — The Actor collects publicly visible job, company, and review information from Naukri and AmbitionBox.
• Responsible use — Only use the data in ways that comply with Naukri's and AmbitionBox's terms of service and applicable law.

Frequently asked questions

Can I scrape Naukri jobs by keyword and city?

Yes. Add titles or skills in searchQueries and Indian cities in locations. Every query is searched in every location; leave locations empty for a nationwide search. sortBy, freshness, and workMode apply to those keyword searches.

Can I schedule Naukri Job Scraper to run automatically?

Yes. Open the Scheduler tab in Apify Console and set a recurring cadence — daily with freshness = "1" for new listings, weekly with freshness = "7" for a market sweep. Create separate schedules per query and city.

What counts as one result?

A job row is one dataset-item at $3.00 per 1,000 jobs. Scrape Job Details adds a job-details event per enriched job. Scrape Company Overview adds a company-overview event once per unique company. Scrape Company Reviews adds a company-reviews event per review. A listing-only run charges only the job-row event plus Actor Start.

Why are some fields empty?

Full description, skills, education, and employmentType are collected only when Scrape Job Details is on. Size, headquarters, and followers appear when Scrape Company Overview is on. The reviews array appears when Scrape Company Reviews is on. Naukri also omits salary on many listings.

How do I avoid duplicate records?

Within one run, listings are deduplicated by jobId. Across runs the Actor appends results and does not merge them. When you combine datasets, keep the first row for each jobId. Setting maxJobs caps any single run.

Do I need a proxy?

The Actor works with the default Apify residential proxy. If a run returns unusually few jobs or keeps timing out, confirm that proxyConfiguration uses the RESIDENTIAL group before raising detailConcurrency.

Can I collect one company's jobs and AmbitionBox reviews?

Yes. Add a Naukri overview URL such as https://www.naukri.com/infosys-overview-13832 or an AmbitionBox URL such as https://www.ambitionbox.com/reviews/infosys-reviews to companyUrls. The Actor collects live jobs up to maxJobs and, when the toggles are on, attaches overview fields and up to maxReviewsPerCompany reviews onto those job rows.

Related Scraptivo automations

• LinkedIn Job Scraper — collect LinkedIn postings for global hiring research alongside Naukri.
• Glassdoor Jobs Scraper — add Glassdoor listings, ratings, and salaries to the same recruiting dataset.
• ZipRecruiter Scraper — pull US job boards when you need coverage beyond India.
• Upwork Jobs Scraper — monitor freelance openings that complement full-time Naukri roles.
• StepStone Jobs Scraper — cover DACH and Benelux job markets with the same workflow.

Support and custom workflows

Need a different field, source, or delivery workflow? Contact Scraptivo at scraptivo@gmail.com. Include the Actor name, sample URL, required fields, and expected volume so we can assess the request.