🔍 LinkedIn Jobs Scraper & Market Intelligence

Production-grade job market data — no login, no cookies, no friction. Extract enriched job listings, salary estimates, skill-demand analytics, and academic-major-matched opportunities from LinkedIn's public endpoints. Purpose-built for AI pipelines, quantitative recruiting, and labor market intelligence at scale.

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Why This Actor Wins in a Crowded Market

Most LinkedIn scrapers in the Apify Store are commoditized — they extract basic title/company/location tuples and call it a day. This Actor is different. It was architected from the ground up as a market intelligence engine, not just a scraper.

Capability	Typical LinkedIn Scrapers	This Actor
Authentication	Require cookies / session tokens	✅ 100% login-free — guest endpoint with session orchestration
Salary Extraction	No or basic regex	✅ Multi-currency parser (6 regex patterns + context-aware period detection)
Skill Tagging	Manual or none	✅ 125+ technical & soft skills auto-extracted from job descriptions
Analytics Layer	Not available	✅ Skill demand ranking, salary distribution, workplace-type breakdown
Academic Major Matching	Not available	✅ Curated skill → job-title mapping for 25+ university majors
Speed Modes	One speed only	✅ Fast Mode (5–10× faster, search-card data) vs. Full Enrichment
Output Quality	Inconsistent fields	✅ 25+ typed fields per job, flat schema, CSV/JSON export ready
AI/LLM Readiness	Raw HTML or sparse JSON	✅ Clean structured output, embedding-ready, RAG-pipeline compatible
Resilience	Often none	✅ Token-bucket rate limiter, exponential backoff, proxy integration, circuit-breaker patterns

The bottom line: If you need raw job listings, any scraper works. If you need enriched, analytics-ready labor market data that feeds directly into an AI pipeline, dashboard, or decision engine — this is the tool.

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Key Features

🎯 Multi-Mode Job Scraping

Mode	What It Does	Best For
search	Single keyword, maximum detail (25+ fields per job)	Targeted job market research
batch_search	Up to 10 keywords in parallel, results merged & deduplicated	Cross-role market comparison
major_search	Auto-generates keyword from academic major (e.g., "Teknik Informatika")	University career centers, edtech platforms
analytics	Runs skill-demand & salary analysis on a keyword — no per-result charge	Labor market intelligence dashboards

💰 Salary Intelligence

• 6 regex patterns covering USD ($102,000 – $159,000), IDR (Rp 8.000.000 – Rp 15.000.000), EUR, GBP, JPY, and bare-number formats
• Context-aware period detection — monthly vs. hourly vs. annual, inferred from surrounding text and currency
• Structured output — salary_min, salary_max, salary_currency, salary_period, salary_raw

📊 Skill Demand Analytics

• 125+ technical skills auto-extracted: programming languages, frameworks, databases, cloud platforms, DevOps tools, ML/AI libraries
• 25+ soft skills detected: Communication, Leadership, Agile, Scrum, Project Management
• Aggregated summaries included at no extra charge when enableAnalytics: true

🎓 Academic Major → Job Matching

Curated skill-to-job-title mapping for 25+ Indonesian university majors. Enter "Teknik Informatika" and the Actor auto-generates an optimized Boolean search query from the most relevant skills, then returns matching job listings.

⚡ Dual Speed Modes

Mode	Speed	Fields Returned	Cost Implication
Full Enrichment (default)	Normal (~20–35s per query)	25+ fields: salary, skills, benefits, description, criteria	Same per-result price, richer data
Fast Mode (fastMode: true)	5–10× faster	Core fields: id, title, company, location, link	Same per-result price, faster throughput

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Input Schema

{
  "mode": "search",
  "keyword": "Data Engineer",
  "location": "Indonesia",
  "maxItems": 25,
  "remoteOnly": false,
  "experienceLevel": "mid",
  "employmentType": "full-time",
  "datePosted": "week",
  "fastMode": false,
  "enableAnalytics": true,
  "proxyConfiguration": {
    "useApifyProxy": true,
    "apifyProxyGroups": ["RESIDENTIAL"]
  },
  "requestTimeoutSecs": 30,
  "debugMode": false
}

Full Parameter Reference

Parameter	Type	Default	Description
mode	enum	"search"	search, batch_search, major_search, or analytics
keyword	string	—	Job title, skill, or Boolean query. Supports AND, OR, NOT, "exact phrase"
keywords	string[]	[]	List of queries for batch_search mode (max 10)
majorName	enum	—	Academic major name for major_search mode (25+ options)
location	string	—	Country, city, or region filter (leave empty for worldwide)
maxItems	integer	10	Results per query (1–25)
remoteOnly	boolean	false	Remote positions only
experienceLevel	enum	—	internship, entry, associate, mid, senior, director, executive
employmentType	enum	—	full-time, part-time, contract, temporary, internship
datePosted	enum	"month"	"24h", "week", or "month"
fastMode	boolean	false	Skip detail fetch — 5–10× faster, returns core fields only
enableAnalytics	boolean	false	Append a free analytics summary record to the dataset
proxyConfiguration	object	Residential	Apify proxy settings (highly recommended)
requestTimeoutSecs	integer	30	HTTP request timeout (10–120 seconds)
debugMode	boolean	false	Enable verbose structured logging

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Enriched Output Sample

Every job listing is pushed to your dataset as a single flat record — no nesting surprises, CSV-export-ready on day one.

{
  "id": 4435506324,
  "title": "Data Engineer",
  "company_name": "TechCorp Indonesia",
  "company_industry": "Technology, Information and Internet",
  "company_logo_url": "https://media.licdn.com/dms/image/...",
  "company_linkedin_url": "https://www.linkedin.com/company/techcorp/",
  "location_city": "Jakarta",
  "location_state": "Jakarta",
  "location_country": "Indonesia",
  "location_formatted": "Jakarta, Indonesia",
  "workplace_type": "Hybrid",
  "employment_type": "Full-time",
  "seniority_level": "Mid-Senior level",
  "job_function": "Engineering",
  "industries": ["Technology", "Information Services"],
  "posted_at": "2026-08-06T00:00:00+00:00",
  "posted_time_ago": "3 days ago",
  "applicant_count": 47,
  "salary_min": 15000000.0,
  "salary_max": 25000000.0,
  "salary_currency": "IDR",
  "salary_period": "monthly",
  "salary_raw": "Rp 15.000.000 - Rp 25.000.000",
  "skills": [
    { "name": "Python", "category": "technical" },
    { "name": "SQL", "category": "technical" },
    { "name": "Apache Airflow", "category": "technical" },
    { "name": "Apache Spark", "category": "technical" },
    { "name": "AWS", "category": "technical" },
    { "name": "Communication", "category": "soft" }
  ],
  "benefits": ["BPJS Kesehatan", "BPJS Ketenagakerjaan", "Remote Work Flexibility"],
  "requirements": [
    "3+ years of experience in data engineering",
    "Strong proficiency in Python and SQL",
    "Experience with Apache Airflow or similar orchestration tools"
  ],
  "description_snippet": "We are looking for a Data Engineer to build and maintain our data platform. You will design ETL pipelines...",
  "easy_apply": true,
  "linkedin_url": "https://www.linkedin.com/jobs/view/4435506324",
  "_query_keyword": "Data Engineer",
  "_query_location": "Indonesia",
  "scraped_at": "2026-08-09T06:00:00+00:00"
}

Analytics Summary (appended when enableAnalytics: true — no extra charge):

{
  "type": "summary",
  "query_keyword": "Data Engineer",
  "query_location": "Indonesia",
  "total_jobs_found": 25,
  "top_skills": "Python, SQL, Apache Spark, AWS, ETL",
  "salary_range": "8000000 – 35000000",
  "workplace_distribution": { "On-site": 12, "Hybrid": 9, "Remote": 4 }
}

---

Quick Integration

Python (Apify SDK)

from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_API_TOKEN")

# Launch the Actor and wait for completion
run = client.actor("galih_rakasiwi/linkedin-job-market-api").call(
    run_input={
        "mode": "search",
        "keyword": "Data Engineer",
        "location": "Indonesia",
        "maxItems": 25,
        "enableAnalytics": True
    }
)

# Fetch results — flat JSON, ready for pandas
jobs = list(
    client.dataset(run["defaultDatasetId"]).iterate_items()
)

# Load into DataFrame for analysis
import pandas as pd
df = pd.DataFrame(jobs)
print(f"Extracted {len(df)} jobs | Salary range: {df['salary_min'].min():.0f} – {df['salary_max'].max():.0f} {df['salary_currency'].mode()[0]}")

cURL (REST API)

curl -X POST "https://api.apify.com/v2/acts/galih_rakasiwi~linkedin-job-market-api/runs?token=YOUR_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "mode": "search",
    "keyword": "Machine Learning Engineer",
    "location": "Singapore",
    "maxItems": 25,
    "datePosted": "week",
    "enableAnalytics": true
  }'

# Fetch results after run completes
curl "https://api.apify.com/v2/datasets/<DATASET_ID>/items?token=YOUR_API_TOKEN&format=json&clean=1"

Node.js (Apify SDK)

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: 'YOUR_APIFY_API_TOKEN' });

const run = await client.actor('galih_rakasiwi/linkedin-job-market-api').call({
    mode: 'batch_search',
    keywords: ['Data Engineer', 'MLOps Engineer', 'Analytics Engineer'],
    location: 'Indonesia',
    maxItems: 15,
    enableAnalytics: true,
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(`Extracted ${items.length} enriched job listings`);

// Filter for high-salary remote roles
const highValue = items
    .filter(j => j.workplace_type === 'Remote' && (j.salary_min || 0) >= 20000000);
console.log(`${highValue.length} remote roles above 20M IDR`);

Apify CLI

apify call galih_rakasiwi/linkedin-job-market-api \
  --input '{"mode": "search", "keyword": "Data Engineer", "maxItems": 25}' \
  --output jobs.json

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Pricing & Support

Item	Price
Each job listing pushed to dataset	$0.02 / result
Analytics summary record	Free — no charge
Failed run / zero results	Free — you pay only for data you receive

You are never billed for compute time, retries, or empty runs. This is pure Pay-Per-Result — transparent, predictable, and aligned with your data needs.

Cost Estimator

Scenario	Estimated Results	Estimated Cost
1 keyword × 10 jobs	10	$0.20
Batch 5 keywords × 10 jobs each	50	$1.00
Major search × 25 jobs	25	$0.50
Analytics mode (skill + salary insights)	1 summary record	Free

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How We Handle Stability & Anti-Bot

LinkedIn's public endpoints are rate-limited and subject to HTML structure changes. This Actor includes multiple layers of defense to maximize uptime and data consistency:

Layer	Mechanism
Token-Bucket Rate Limiter	Throttles requests to ~8–10 req/s, preventing burst patterns that trigger IP blocks
Exponential Backoff Retry	3 retries with 1s → 2s → 4s backoff on TimeoutException, ConnectError, RemoteProtocolError
Auth Wall Detection	Monitors redirects to /login, /checkpoint, /signup — surfaces clean errors instead of silent data loss
HTTP 429 Handler	Respects Retry-After headers when LinkedIn rate-limits, backing off gracefully
Session Warmup	Establishes guest cookies via a dedicated warmup request before any data fetch
Proxy Integration	First-class Apify Residential Proxy support — rotates IPs to avoid geographic and volumetric blocking
Partial Result Resilience	If one job detail fetch fails, the rest of the batch continues — no all-or-nothing failures
CSS Selector Constants	All HTML selectors are module-level constants. When LinkedIn changes markup, only selectors update — parsing logic stays intact
Circuit Breaker Ready	Detects consecutive failures and can halt requests during cool-down periods (configurable)

This isn't a naive requests.get() loop. It's a production-grade HTTP client with session orchestration, originally built as a FastAPI microservice with structured logging, type safety (mypy strict mode), and >80% test coverage on parser logic.

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Perfect For

Audience	Use Case
Quantitative Researchers	Labor market econometrics, salary trend modeling, skill-demand forecasting
Data Scientists & ML Engineers	Build job recommendation engines, train skill-gap classifiers, feed RAG pipelines
AI Developers	Power AI Career Agents with real-time structured job data + skill analytics
HR Tech & Recruiting Platforms	Backend job feed, competitive hiring intelligence, salary benchmarking
Universities & EdTech	Career center job matching, curriculum-to-market alignment analysis
Job Boards & Aggregators	Structured job feed with 25+ fields — no parsing needed
Government & Policy Research	Labor market analysis, workforce planning, employment policy data

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Tech Stack

FastAPI · httpx (HTTP/2) · BeautifulSoup4 + lxml · Pydantic v2 · Apify SDK (Python) · structlog (structured JSON logging) · MongoDB (optional, for major_search mode)

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Development & Self-Hosting

This Actor is built on a standalone FastAPI microservice that can run independently outside Apify:

# Clone and set up
git clone <repo-url> && cd linkedin_api
uv sync

# Run as Apify Actor (local simulation)
uv run python src/main.py

# Run as REST API server
uv run uvicorn app.main:app --reload

Full technical documentation — architecture, data flow pipeline, parser strategy, configuration reference, and testing guide — is available in docs/DOCUMENTATION.md.

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Disclaimer

This Actor accesses publicly available data from LinkedIn's guest (non-authenticated) endpoints. It does not bypass authentication, scrape logged-in content, or violate LinkedIn's robots.txt directives.

• Data Rights: All job listing data remains the property of the respective employers and LinkedIn Corporation. Users are responsible for complying with LinkedIn's Terms of Service and applicable data protection laws in their jurisdiction.
• No Affiliation: This Actor is not affiliated with, endorsed by, or connected to LinkedIn Corporation or Microsoft.
• Accuracy: Salary data is extracted from job descriptions using heuristic parsing. Not all listings include salary information — null values indicate the employer did not disclose compensation.
• Rate Limits: LinkedIn's guest API imposes a soft limit of ~50–75 results per query. The Actor respects these limits and surfaces them transparently.

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License

MIT — see LICENSE.