LinkedIn Jobs Scraper Enhanced

Scrape LinkedIn job postings with descriptions, skills, salary signals, and company data.

Why This Actor Is Better

This actor is engineered to outperform competitors on reliability, data completeness, output quality, and transparent pricing.

Competitor issue	This actor advantage
Single data source	Multi-source extraction + enrichment fallback
No confidence scoring	0-1 confidence score on every result
No source attribution	Full source tracking per dataset row
Slow / blocking	Automatic retries, proxy rotation, cache-first mode
Missing enrichment	Built-in contact, tech-stack, and sentiment enrichment
Requires paid APIs	No external API keys required for core extraction

Key Features

• Job title, company, location, and description
• Seniority, employment type, and remote options
• Skill extraction and salary signals where visible
• Posted/updated timestamps
• Batch search query input

Architecture

flowchart LR
    A[Input Query/URL] --> B[Playwright/HTTP Scraper]
    B --> C{Success?}
    C -->|Yes| D[Parse & Normalize]
    C -->|No| E[Retry / Proxy Rotate]
    E --> B
    D --> F[Enrichment Engine]
    F --> G[Confidence Scoring]
    G --> H[Apify Dataset]
    H --> I[Webhook / CSV Export]

1. Input ingestion — Accept URLs, queries, or batch lists via the Apify Console.
2. Resilient extraction — Playwright or HTTP-based scraping with retries and proxy rotation.
3. Parsing & normalization — Transform raw platform data into consistent JSON.
4. Enrichment — Append contact signals, tech stacks, sentiment, or cross-platform data where available.
5. Confidence scoring — Each record receives a 0-1 reliability score.
6. Dataset output — Results are pushed to the Apify dataset with source attribution.

Input

Parameter	Type	Required	Default	Description
mode	string	—	enrich	Execution mode: fast_lookup (cache-first), enrich (multi-...
queries	array	✅	...	List of search queries, URLs, or usernames to process.
maxResults	integer	—	25	Maximum number of results to return per query (1-1000).
webhookUrl	string	—		Optional URL to receive a POST notification when the acto...

Output

Results are stored in the Apify dataset as structured JSON objects. See .actor/output_schema.json for the full schema.

Field	Type	Description
query	string	Input query, URL, or search term used to drive the scrape
jobTitle	string	Job title or role
company	string	Company or organization name
companyUrl	string	companyUrl value
location	string	Location, city, or address
jobUrl	string	jobUrl value
description	string	description value
employmentType	string	employmentType value
seniority	string	seniority value
remoteStatus	string	remoteStatus value
salary	string	salary value
skills	string	skills value
postedDate	string	postedDate value
applicants	string	applicants value
confidenceScore	number	0-1 confidence score for the result
scrapedAt	string	ISO timestamp when the row was scraped

Pricing

This actor uses transparent pay-per-event (PPE) pricing. You only pay for successful results.

Plan	Price per 1K results	Savings
Leading competitors	~$2.39/1K	—
This actor	$1.77/1K	26% cheaper

A small one-time Actor Start fee applies per run. No monthly subscription required.

Use Cases

• Recruiting intelligence — track competitor hiring
• Job market analysis — map demand by skill and city
• Sales timing — trigger outreach when companies post hiring signals
• Career research — analyze salary ranges and requirements
• Workforce planning — benchmark role titles and growth

Legal & Compliance

This actor scrapes publicly available data from LinkedIn. It does not access private data, bypass authentication, or store credentials. Users are responsible for complying with applicable data protection laws (GDPR, CCPA, etc.) and the platform's Terms of Service.

This actor is intended for legitimate research, analysis, and lead-generation use cases. It must not be used for spam, harassment, or unlawful activity.

AI-DLC / Data Lifecycle

This actor follows AI-DLC principles for ethical data handling:

• Collection — Public data only; respects robots.txt and rate limits.
• Processing — In-memory normalization; no PII logging.
• Storage — Results are pushed to the user's Apify dataset, not retained by the actor.
• Usage — Designed for analysis, enrichment, and legitimate outreach.
• Disposal — No long-term caching of user data between runs.

Enhancement Roadmap (API / MCP Integrations)

• Add skill-demand MCP to map trending technologies
• Integrate salary-benchmark MCP (Levels.fyi/PayScale) for pay ranges
• Add company-enrichment MCP (Crunchbase/Clearbit) for account context

Changelog

• v1.0 — Initial release with core scraping, structured output, confidence scoring, and pay-per-event pricing.
• v1.1 — Premium README, full output schema, and AI-DLC documentation added.