How to Scrape Naukri.com Jobs in Python (Structured JSON with Salaries)

Naukri.com is India’s largest job board with over 100,000 active listings at any time. With no official API, scraping is the only way to get structured job data at scale. Here is the complete guide.

Why Naukri data matters

Naukri is where most Indian companies post jobs first. Unlike LinkedIn, Naukri captures:

• Mid-market and non-tech companies that do not use LinkedIn for hiring
• Salary ranges displayed openly (not hidden behind “Sign in to see”)
• Experience ranges and required skill lists per listing
• Work mode (WFH, hybrid, office) clearly indicated

For hiring intelligence and labour market research in India, Naukri is the primary source.

What each listing contains

• Job title and company name
• Salary range in lakhs per annum (normalised from various display formats)
• Experience range required (e.g. “3-7 years”)
• Required skills list
• Location (city or cities)
• Work mode: work from home, hybrid, or office
• Direct Naukri application URL

Using the Apify actor

import apify_client

client = apify_client.ApifyClient('YOUR_APIFY_TOKEN')

run_input = {
    "searchQuery": "data engineer",
    "location": "Bangalore",
    "experienceMin": 2,
    "experienceMax": 6,
    "maxResults": 200,
}

run = client.actor('themineworks/naukri-jobs').call(run_input=run_input)

for job in client.dataset(run['defaultDatasetId']).iterate_items():
    print(f"{job['title']} at {job['company']}")
    print(f"  Salary: {job['salary']}  Experience: {job['experience']}")
    print(f"  Skills: {', '.join(job['skills'][:5])}")
    print(f"  {job['location']}  {job['work_mode']}")
    print(f"  {job['url']}")

Salary benchmarking by role

Aggregate salary data across listings to build a benchmark by role and city:

import statistics
results = list(client.dataset(run['defaultDatasetId']).iterate_items())

# Extract min salary values for jobs with salary data
salaries = []
for job in results:
    if job.get('salary_min_lpa'):
        salaries.append(job['salary_min_lpa'])

if salaries:
    print(f"Data Engineer salaries in Bangalore:")
    print(f"  Median: {statistics.median(salaries):.1f} LPA")
    print(f"  P25: {sorted(salaries)[len(salaries)//4]:.1f} LPA")
    print(f"  P75: {sorted(salaries)[3*len(salaries)//4]:.1f} LPA")

Skills demand analysis

Find the most in-demand skills in your target role:

from collections import Counter

all_skills = []
for job in results:
    all_skills.extend(job.get('skills', []))

skill_counts = Counter(all_skills)
print("Top 15 skills for data engineers in Bangalore:")
for skill, count in skill_counts.most_common(15):
    pct = count / len(results) * 100
    print(f"  {skill}: {count} jobs ({pct:.0f}%)")

Competitor hiring monitoring

Track when a competitor starts hiring heavily in a new function:

run_input = {
    "company": "Flipkart",          # monitor specific company
    "maxResults": 500,
    "includeAllLocations": True,
}

WFH and work mode breakdown

from collections import Counter

modes = Counter(job.get('work_mode', 'Not specified') for job in results)
for mode, count in modes.most_common():
    print(f"  {mode}: {count} ({count/len(results)*100:.0f}%)")

Scheduling for market tracking

Run weekly to track how demand for specific skills or roles changes over time. Combine with a time-series database for trend charts.

Pricing

Pay per job returned. Zero charge on empty searches.