Track India IT Services Hiring on Naukri (2026 Guide)

Thirdwatch's Naukri.com Scraper can persist a watchlist and return only newly discovered jobs on later runs. Use it to measure hiring volume, locations, experience bands and skill demand without repeatedly paying for unchanged rows.

▶ 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 track IT services hiring on Naukri

Large IT-services employers publish roles across many cities, business units and skill families. A consistent feed of newly posted jobs gives researchers a defensible way to compare hiring volume and mix over time. It does not, by itself, prove why a company is hiring.

The job-to-be-done is structured. An equity analyst covering Indian IT wants weekly posting velocity per firm with leading-indicator alerts. A competitive-intelligence team at one IT services firm monitors peer hiring to detect contract wins or strategic shifts. A labour-market researcher studies which experience bands (fresher vs lateral hiring) each firm is targeting. A tech-skills training company studies which skills are being adopted by Indian IT services firms to inform curriculum updates. All reduce to weekly Naukri snapshots × tier-1 IT firms × structured analysis.

How does this compare to the alternatives?

Three options for getting Indian IT services hiring intelligence:

Quarterly disclosures give official confirmation but lag the leading-indicator window. The Naukri Scraper actor page gives you the structured weekly feed at pay-per-result pricing.

How to track IT services hiring 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 take a weekly snapshot of all major IT services firms?

Build city × firm queries. Naukri encodes both in the search query string.

import os, requests, datetime, json, pathlib

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

CITIES = ["bangalore", "mumbai", "delhi", "hyderabad",
          "chennai", "pune", "kolkata"]
FIRMS = ["tcs", "infosys", "wipro", "hcl",
         "tech mahindra", "ltimindtree", "cognizant"]

queries = [f"{firm} {city}" for firm in FIRMS for city in CITIES]
print(f"Submitting {len(queries)} queries (7 firms × 7 cities)")

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": False,
        "monitorMode": "new-jobs",
        "monitorStoreName": "naukri-it-services-watch",
    },
    timeout=3600,
)
records = resp.json()
week = datetime.date.today().isocalendar()
ts = f"{week.year}-W{week.week:02d}"
pathlib.Path(f"snapshots/naukri-it-{ts}.json").write_text(json.dumps(records))
print(f"{ts}: {len(records)} listings")

The first run establishes the baseline. Later runs return only jobs not present in the monitor's persistent history. Keep the store name stable and avoid overlapping runs that use the same store.

Step 3: How do I compute weekly posting velocity per firm?

Aggregate snapshots by week and firm; count unique apply URLs.

import pandas as pd, glob

frames = []
for f in sorted(glob.glob("snapshots/naukri-it-*.json")):
    week = pathlib.Path(f).stem.replace("naukri-it-", "")
    for j in json.loads(pathlib.Path(f).read_text()):
        cn = (j.get("company_name") or "").lower()
        for firm in FIRMS:
            if firm in cn:
                frames.append({"week": week, "firm": firm,
                               "url": j["apply_url"],
                               "experience": j.get("experience"),
                               "skills": j.get("skills", [])})
                break

df = pd.DataFrame(frames).drop_duplicates(subset=["week", "url"])
weekly = df.groupby(["week", "firm"]).size().reset_index(name="postings")
pivot = weekly.pivot(index="firm", columns="week", values="postings").fillna(0)
weeks = sorted(pivot.columns)
if len(weeks) >= 5:
    pivot["wow_pct"] = (pivot[weeks[-1]] / pivot[weeks[-5:-1]].mean(axis=1).clip(lower=1)) - 1
    print(pivot[[weeks[-1], "wow_pct"]].sort_values("wow_pct", ascending=False))

Use percentage changes as alert thresholds for manual review, not as proof of ramp-up or contraction. Small baselines can make percentage changes look dramatic.

Step 4: How do I detect skill-mention shifts and contract-win signals?

Aggregate skill arrays by firm and week, surface fastest-growing skills.

import re

# Skill arrays exploded
skill_frames = []
for _, row in df.iterrows():
    skills = row.skills if isinstance(row.skills, list) else []
    for s in skills:
        skill_frames.append({"week": row.week, "firm": row.firm,
                             "skill": s.lower().strip()})

skill_df = pd.DataFrame(skill_frames)
weekly_skills = skill_df.groupby(["week", "firm", "skill"]).size().reset_index(name="mentions")

# Fastest-growing skills per firm over the last 8 weeks
for firm in FIRMS:
    firm_skills = weekly_skills[weekly_skills.firm == firm]
    pivot_s = firm_skills.pivot(index="skill", columns="week", values="mentions").fillna(0)
    if len(pivot_s.columns) < 8:
        continue
    weeks_s = sorted(pivot_s.columns)
    pivot_s["growth"] = pivot_s[weeks_s[-1]] - pivot_s[weeks_s[-8]]
    rising = pivot_s[pivot_s.growth >= 5].sort_values("growth", ascending=False).head(5)
    print(f"\n--- {firm.upper()}: rising skills (8-week growth) ---")
    print(rising[[weeks_s[-8], weeks_s[-1], "growth"]])

If posting volume and related skill mentions move together, flag the company and location for deeper research. Confirm the cause with company disclosures, tender notices or other primary evidence.

Sample output

A single record from the dataset for one Bangalore TCS posting looks like this. Five rows of this shape weigh ~25 KB.

{
  "title": "Software Engineer",
  "company_name": "Tata Consultancy Services",
  "location": "Bengaluru",
  "salary_raw": "Not disclosed",
  "experience": "2-5 Yrs",
  "skills": ["Java", "Spring Boot", "Microservices", "AWS"],
  "description": "Looking for a Software Engineer with experience in Java...",
  "posted_at": "1 day ago",
  "apply_url": "https://www.naukri.com/job-listings-software-engineer-tcs-..."
}

apply_url is the canonical natural key for cross-snapshot dedup. experience (2-5 Yrs) is the experience-band signal — parse the lower bound to bucket into Junior/Mid/Senior tiers. skills is a clean string array — much higher-signal than parsing description text. salary_raw is Not disclosed for the majority of IT services postings (cultural norm in Indian IT services); for compensation analysis layer in AmbitionBox data instead.

Common pitfalls

Three things go wrong in production IT services tracking pipelines. Subsidiary attribution — Tata Consultancy Services posts both as "TCS" and "Tata Consultancy Services" in different listings; build a per-firm name-variation map (TCS / Tata Consultancy Services / TATA / Tata Consultancy) before company aggregation. Multi-city listings — IT services firms frequently post the same role across multiple cities ("Bengaluru, Hyderabad, Pune"); dedupe by apply_url rather than counting per-city occurrences. Fresher vs lateral mix shifts — TCS's 0-1 Yrs band reflects campus-recruitment placements more than open-market hiring intent; for genuine market-hiring intelligence focus on 3-7 Yrs experience-band postings rather than freshers.

Thirdwatch's actor uses Naukri's web endpoints over HTTP, supports pagination up to 1,000 results per query, and can enrich rows with descriptions when needed. Monitoring runs fail closed: if a configured search is incomplete, the saved history is not advanced. Pair Naukri with our AmbitionBox Scraper when you need employer-review context.

Related use cases

• Scrape Naukri jobs for India recruiting
• 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