LinkedIn Decision Makers Scraper

Find CEOs, founders, CTOs, recruiters, and other LinkedIn decision makers by job title, industry, and location — no cookies or LinkedIn login required.

Paste a few job titles, optionally narrow by industry and country, and walk away with a clean spreadsheet of names, current roles, companies, locations, and LinkedIn profile URLs — ready for your CRM, sequencer, or applicant-tracking system.

No browser extensions. No LinkedIn account. No Sales Navigator seat. Just a single Apify actor that runs in the cloud and returns publicly visible profile data.

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What it does

This actor turns LinkedIn into a B2B prospecting source. Specify the decision-maker titles you want (CEO, Founder, CTO, VP Sales, Marketing Director, Recruiter, …), optionally add industry tags (SaaS, Fintech, Healthcare) and locations (United States, Berlin), and it returns a clean dataset of matching profiles.

It is positioned as an affordable LinkedIn Sales Navigator alternative for outbound sales, recruiting, and competitive-intelligence work.

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What you get back

Each row in the dataset is one decision maker:

{
    "fullName": "John Smith",
    "firstName": "John",
    "lastName": "Smith",
    "headline": "CEO at AI Growth Labs",
    "currentRole": "CEO",
    "company": "AI Growth Labs",
    "location": "New York, United States",
    "linkedinUrl": "https://www.linkedin.com/in/johnsmith",
    "profileImage": null,
    "snippet": "CEO at AI Growth Labs helping SaaS companies...",
    "jobTitleMatched": "CEO",
    "industryKeyword": "SaaS",
    "locationKeyword": "United States",
    "timestamp": "2026-05-21T12:00:00Z"
}

The full field list:

Field	What it is
fullName	The decision maker's full name
firstName / lastName	Best-effort split for mail-merge
headline	LinkedIn headline
currentRole	Detected role (CEO, Founder, CTO, …)
company	Detected company name
location	City / region / country
linkedinUrl	Canonical https://www.linkedin.com/in/<slug> URL
snippet	Raw profile snippet text
jobTitleMatched	The decision-maker title bucket this profile matched
industryKeyword	Industry filter used (if any)
locationKeyword	Location filter used (if any)
timestamp	ISO 8601 scrape time

Export to CSV, JSON, Excel, or pipe directly into Zapier, Make, Clay, Apollo, or any HTTP webhook.

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Inputs

Field	Type	What it does
Decision Maker Titles	array<string>	Titles to hunt for (CEO, Founder, CTO, VP Sales, …). Defaults to CEO, Founder, CTO.
Industry / Company Keywords	array<string>	Industry tags (SaaS, Fintech) or company names (Stripe, OpenAI). Optional.
Locations	array<string>	Country / region / city filters (United States, Berlin). Optional.
Maximum Profiles	integer	Stop after collecting N unique profiles. Default 100.
Search Language	string	Language hint. Default en.
Deduplicate Results	boolean	Drop duplicate LinkedIn URLs across the run. Default true.

The actor automatically expands every combination of (industries × locations) using the supplied title bucket. So 3 industries × 2 locations = 6 internal searches, all deduplicated into one clean dataset.

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Example use cases

Outbound B2B sales — Pull a list of 200 SaaS founders in the United States in under a minute, then push them into Apollo or Clay for email enrichment.

Recruiting & sourcing — Build a roster of every recruiter in healthcare across Germany and France. Pair linkedinUrl with a profile-scraper actor to enrich work history.

SaaS founders prospecting investors — Search Partner, Principal, Managing Director across venture-capital firms for warm-intro targets.

HR agencies — Find HR managers and CHROs by industry vertical, then run scheduled scrapes monthly to track leadership churn at target accounts.

Competitive intel for RevOps — Map the head of growth, head of marketing, and CTO at every direct competitor in one run.

Outreach agencies — Generate fresh decision-maker lists per client per industry, no Sales Navigator seat per agent.

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Tips that save you time and money

• Preview with a low cap. Set Maximum Profiles to 25 for your first run to verify the queries return the kind of profiles you expect.
• Be specific with industries. "SaaS" returns very different people than "B2B SaaS" or "vertical SaaS". Use quoted phrases to keep the matching tight.
• Locations are loose by design. Treat them as a phrase that appears anywhere on the profile, so you'll get some noise. For a clean per-country list, run separate location queries and filter the output.
• Mix in company names. Adding "Stripe" as a keyword finds people working at Stripe or who mention Stripe in their headline — useful for ABM list-building.
• Deduplication is on by default. Leave it on unless you specifically want to see overlap across keywords.

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Pricing — pay only for what you extract

Apify Plan	Start Fee	Per Profile
Free / Bronze	$0.0025	$0.0025
Silver	$0.0025	~$0.0022
Gold	$0.0025	~$0.0020
Platinum	$0.0025	~$0.0018
Diamond	$0.0025	~$0.0015

Real-World Cost Examples 💰

• 25 profiles for a quick test run: about $0.06
• 250 SaaS founders in the US & Canada: about $0.63
• 2,000 decision makers across multiple industries: about $5.00

Affordable lead generation at scale without expensive LinkedIn tools 🚀

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Frequently asked

Do I need a LinkedIn account or Sales Navigator? No. The actor never logs into LinkedIn, never asks for cookies, and never touches your account. It returns publicly visible profile data only.

Will this get me LinkedIn-banned? No. The actor never authenticates against LinkedIn — there's no account activity for LinkedIn to flag.

How accurate is the role/company parsing? Very good for headlines in the standard "<Role> at <Company>" format, which is how most LinkedIn profiles are written. Multilingual headlines ("CEO chez …", "PDG bei …") are also detected. Obscure formats may leave the role/company fields null — the raw headline and snippet are always preserved so you can post-process.

Will I get duplicates if I run it twice? Within a single run, no — dedup is on by default. Across runs, yes — track previously seen linkedinUrl values in your own pipeline.

Can I schedule this? Yes. Apify Schedules let you run it weekly to track new decision makers entering an industry or location. Combine with a webhook to push new rows into Google Sheets, Notion, HubSpot, or Salesforce.

The actor returned 0 results — what's wrong? Check in this order: (1) Are your industry/location keywords reasonable English phrases that would appear on a public profile? (2) Try removing the Locations filter to see if the issue is over-narrowing. (3) Broaden your title list — add synonyms like Co-Founder next to Founder.

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A note on responsible use

This actor extracts data that is already publicly visible on LinkedIn. It does not bypass authentication, log into accounts, scrape private profiles, or pull anything beyond what any anonymous visitor could see. The legal foundation for scraping public profile data was reinforced in hiQ Labs v. LinkedIn (9th Circuit, 2022).

That said: scraped data is still personal data. Don't use it for spam, harassment, or anything that violates GDPR, CCPA, or similar laws in your jurisdiction. Treat the people in your dataset the way you'd want to be treated in someone else's CRM.

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Drop it into your workflow

• Apify Schedules → run weekly to catch newly visible decision makers in your target industries
• Zapier / Make → push new rows into HubSpot, Salesforce, Notion, or Google Sheets
• Apify Webhook → POST results to your server for custom enrichment
• Pair with a LinkedIn Profile Scraper → use linkedinUrl to enrich each lead with full work history
• Pair with an email-finder → push firstName + lastName + company into Clay, Apollo, or Hunter to resolve emails

Run it once with a small maxResults to see what you get. The first 25 profiles are usually cheap enough to taste-test before committing.