Scrape Naukri Jobs for India Recruiting at Scale (2026)

Thirdwatch's Naukri.com Scraper returns India's largest job board's listings — title, company, location, salary, experience, skills, full descriptions across 20+ Indian cities. Built for India-focused recruiter agencies, Indian tech startups building their own talent attraction, salary-research analysts, and aggregator builders who need Naukri's catalog as a structured feed without per-call API billing.

▶ Skip the setup: Run this as a ready-to-go task on Apify → — pre-loaded with the exact configuration from this guide. No code required.

Why scrape Naukri for Indian recruiting

Naukri is the dominant Indian job board. According to Naukri parent InfoEdge's 2024 annual disclosures, the platform processes more than 50 million monthly visits and indexes 80%+ of the Indian recruiting catalog — material depth no other source matches in India. For any India-focused pipeline (recruiter agency, in-house TA team, salary-research analyst), Naukri is the primary source. The blocker for systematic access: Naukri does not offer a public job-search API for third-party developers.

The job-to-be-done is structured. An India-focused recruiter agency wants daily refresh of every senior backend engineer role across Bangalore, Mumbai, and Delhi NCR. A startup HR leader monitoring competitor postings wants Naukri data for the 30 companies they actively poach from. A salary-research analyst tracks Indian tech compensation by experience band over time. An aggregator builder ingests Naukri alongside LinkedIn and Indeed for the broadest possible India coverage. All reduce to query × maxResults pulls returning structured rows ready for pandas, BigQuery, or a CRM.

How does this compare to the alternatives?

Three options for getting Naukri data into a pipeline:

Indian recruitment SaaS bundles Naukri with LinkedIn and other sources behind enterprise contracts. The Naukri Scraper actor page gives you the structured feed at pay-per-result pricing — no minimum, no contract.

How to scrape Naukri jobs in 4 steps

Step 1: How do I authenticate against Apify?

Sign in at apify.com (free tier, no credit card), open Settings → Integrations, and copy your personal API token. Every example below assumes the token is in APIFY_TOKEN:

export APIFY_TOKEN="apify_api_xxxxxxxxxxxxxxxx"

Step 2: How do I pull jobs across multiple cities and roles?

Build a city × role query matrix and pass it to the actor. The actor paginates automatically until it reaches maxResultsPerQuery or the source has no more rows.

import os, requests, pandas as pd

ACTOR = "thirdwatch~naukri-jobs-scraper"
TOKEN = os.environ["APIFY_TOKEN"]

CITIES = ["bangalore", "mumbai", "delhi", "hyderabad",
          "chennai", "pune", "kolkata", "ahmedabad"]
ROLES = ["software engineer", "data scientist", "product manager",
         "devops engineer", "frontend developer"]

queries = [f"{role} {city}" for city in CITIES for role in ROLES]
print(f"Submitting {len(queries)} queries")

resp = requests.post(
    f"https://api.apify.com/v2/acts/{ACTOR}/run-sync-get-dataset-items",
    params={"token": TOKEN},
    json={"queries": queries, "maxResultsPerQuery": 100, "includeDescription": True},
    timeout=3600,
)
df = pd.DataFrame(resp.json())
print(f"{len(df)} listings across {df.company_name.nunique()} companies")

With maxResultsPerQuery: 100, this matrix can return up to 4,000 unique listings. Start with a smaller watchlist, inspect overlap, and expand only where the additional coverage is useful.

Step 3: How do I parse salary strings into numeric lakhs?

Naukri publishes salaries in lakhs format like 25-30 Lacs PA; regex-parse for analytics.

import re

def parse_lakhs(s):
    if not s or "Not disclosed" in str(s):
        return (None, None)
    nums = re.findall(r"(\d+(?:\.\d+)?)", str(s))
    if len(nums) < 1:
        return (None, None)
    if len(nums) == 1:
        return (float(nums[0]), float(nums[0]))
    return (float(nums[0]), float(nums[1]))

df[["lakhs_min", "lakhs_max"]] = df.salary_raw.apply(
    lambda s: pd.Series(parse_lakhs(s))
)
disclosed = df[df.lakhs_min.notna()]
print(f"Salary disclosed on {len(disclosed)}/{len(df)} listings ({len(disclosed)/len(df):.0%})")

ranked = (
    disclosed.groupby("company_name")
    .agg(median_lakhs=("lakhs_max", "median"),
         n=("title", "count"))
    .query("n >= 3")
    .sort_values("median_lakhs", ascending=False)
)
print(ranked.head(20))

The output is the company-by-company Indian compensation leaderboard for your role-and-city watchlist.

Step 4: How do I push to a recruiter CRM with deduplication?

Naukri's apply_url is stable per posting; dedupe on it and upsert to HubSpot or your sourcing tool.

df = df.drop_duplicates(subset=["apply_url"])

import psycopg2.extras
with psycopg2.connect(...) as conn, conn.cursor() as cur:
    psycopg2.extras.execute_values(
        cur,
        """INSERT INTO naukri_jobs
              (apply_url, title, company_name, location,
               salary, experience, skills, description,
               posted_at, scraped_at)
           VALUES %s
           ON CONFLICT (apply_url) DO UPDATE SET
             title = EXCLUDED.title,
             salary = EXCLUDED.salary,
             scraped_at = now()""",
        [(j["apply_url"], j["title"], j["company_name"], j.get("location"),
          j.get("salary_raw"), j.get("experience"), j.get("skills"),
          j.get("description"), j.get("posted_at"), "now()")
         for j in df.to_dict("records")],
    )

Schedule the actor on Apify's scheduler at daily cadence and the loop is fully self-maintaining.

Sample output

A single record for one Bangalore software engineer role looks like this. Five rows of this shape weigh ~25 KB.

{
  "title": "Software Engineer",
  "company_name": "Cisco",
  "location": "Bengaluru, Hyderabad",
  "salary_raw": "Not disclosed",
  "experience": "0-1 Yrs",
  "skills": ["Python", "Java", "Agile", "Unit Testing"],
  "description": "Please note this posting is to advertise potential job opportunities...",
  "posted_at": "1 day ago",
  "apply_url": "https://www.naukri.com/job-listings-software-engineer-cisco-..."
}

apply_url is the canonical natural key for upsert. salary_raw is whatever Naukri shows publicly — 25-30 Lacs PA for disclosed salaries or Not disclosed (which the actor preserves verbatim). experience is a free-text band like 0-1 Yrs, 5-8 Yrs, or 10+ Yrs — parse the lower bound out via regex for filtering. skills is a clean array of strings, much higher signal than keyword extraction from descriptions.

Common pitfalls

Three things go wrong in production Naukri pipelines. Salary opacity — many listings show Not disclosed; always filter those rows before calculating compensation statistics. City-name spelling drift — related location spellings can produce overlapping result sets, so standardise your query matrix and deduplicate by job_id or apply_url. Overly broad pagination — the actor can request up to 1,000 results per query, but narrower role-and-location queries usually produce cleaner, more actionable feeds.

Thirdwatch's actor uses direct HTTP endpoints rather than a browser, which keeps compute cost low and throughput predictable. It supports Naukri India and Naukrigulf, up to 1,000 results per query, optional detail enrichment and persistent new-job monitoring. Pair it with our AmbitionBox Scraper for employer-review context, CutShort Scraper for startup tech jobs, and LinkedIn Jobs Scraper for international coverage.

Related use cases

• Track India IT-services hiring on Naukri
• Build Naukri salary benchmarks by experience
• Find India tech jobs by skill on Naukri
• The complete guide to scraping job boards
• All Thirdwatch use-case guides