Export Naukri Jobs to CSV and Find the Skills Employers Request

A job seeker asks which skills to study next. A recruiter wants to compare open roles. A training team needs evidence for a course update. Each can begin with the same useful material: a clearly scoped sample of actual job listings.

The difficult part is keeping the sample honest. Repeated search matches are not extra vacancies, and missing salary information should remain missing.

My Naukri Jobs Scraper exports public listings with available descriptions, skills, experience requirements, work mode, company identifiers, and disclosed salaries.

Write the research question first

Choose one role family, one location or location strategy, and a recent period. "What do recent Python developer listings in Bengaluru request?" is small enough to inspect.

A broad query across unrelated roles makes skill counts hard to interpret. Keep separate search labels if you compare roles or cities later.

Export a small sample with details

This request illustrates a focused run:

{
  "keyword": "python developer",
  "location": "Bengaluru",
  "freshnessDays": 7,
  "maxJobs": 50,
  "fetchDetails": true
}

The actor documentation also describes multiple searches, supported search URLs, and individual job lookups. Choose one input group rather than mixing unrelated entry methods.

Open the dataset and collection report after the run. Inspect a handful of descriptions and application links against the source. A job cap bounds your sample; it does not measure every available vacancy.

Keep unique jobs separate from query matches

The same listing can match multiple role keywords or searches. Preserve job identity and search labels, then count unique listings for your main analysis.

When the actor provides matchedQueryIds, use them to explain how a job entered the sample. Do not count each match as a separate role.

Company names also need care. An employer-name query does not establish an exact employer match. Use company identifiers when identity matters, and inspect the direct-employer options before comparing corporate hiring activity.

Build a skill table you can review

Keep source descriptions intact. Create your own normalized skill table beside them:

Use a documented mapping for aliases. Keep ambiguous terms separate until a reviewer resolves them.

Count a skill once per unique listing when answering "how many sampled listings mention this?" Repeating a keyword five times in one description should not count as five employers requesting it.

Preserve the salary disclosure boundary

A missing salary is unknown, not zero. A posted range is advertised information, not proof of what someone was offered or paid.

If you summarize disclosed ranges, report how many listings disclosed usable values, preserve the stated units and period, and keep unlike compensation formats separate. Avoid filling gaps with estimates while presenting the result as scraped source data.

Similarly, one listing can cover several openings, and multiple listings can describe similar work. Listing counts should stay listing counts unless the source establishes more.

Produce a short hiring memo

For your initial sample, report the query, collection date, unique listing count, usable detail count, and scope limits. Then show the most frequent explicit skills with their sample denominator.

Add a few source examples to explain surprising combinations. A Python role that also asks for frontend experience may describe a broader job than its title suggests.

For a job seeker, turn the findings into a study question: "Which recurring skill can I demonstrate in a small project?" For a recruiter, use them to review your own role description against the sampled market.

Refresh without losing context

Save each run's input and observation date. Track newly observed job IDs separately from existing records, and retain source freshness labels. Re-observing a listing does not prove that the employer opened a new position that day.

Begin with one role and 50 results in the Naukri Jobs Scraper. A clean, clearly scoped skills table gives you a useful discussion before you expand to a broader hiring feed.