Welcome to the Jungle · Hiring Companies & Profiles

Paste a WTTJ slug (enky) or leave the list empty and discover companies hiring. One row per company: website, company LinkedIn, open roles, profile stats and featured team when WTTJ publishes them, plus funding and founder LinkedIn on international profiles. Optional named-person hop and Lemlist. CSV / JSON / Excel.

No login. No API key. No WTTJ account needed.

Mode	Input	Best for
Enrich	companyUrls: enky, payfit (or the company profile URL)	You already have accounts — we load the public profile, stats, team page and jobs
Discover	Sectors + job types (URLs empty)	You need a new pipeline of companies that are hiring

The Actor searches both WTTJ job boards in discover mode (classic listings and the international jobs board). Every row includes the company website and company LinkedIn when WTTJ has them.

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Who is this for?

You are…	Typical goal	Suggested setup
B2B SaaS / software vendor	Find tech companies scaling their sales team	Sectors: SaaS + Software · Job types: Sales · Contract: Full-time · Last 3 months
Recruiter / headhunter	Build a pipeline of companies hiring in your niche	Sector of your choice · Contract: Full-time or Apprenticeship
Outbound SDR / BDR	Identify companies with budget signal (active hiring)	Any sector · Job type: Sales or Tech · Company LinkedIn is already on every row
Account-based / CRM	Enrich companies you already picked	Paste slugs: enky, payfit — filters ignored
Market researcher	Map which sectors are hiring and where	Multiple sectors · No job type filter · No cap
HR tech vendor	Find companies posting HR / People roles	Sectors: any · Job types: HR / People
Investor / analyst	Track hiring activity as a growth signal	One sector · Last 1–3 months · Download full dataset

What you get by default: company name, WTTJ profile URL, company website, company LinkedIn, open roles, hiring mix (sales vs tech vs marketing), headcount, average age / gender split / published revenue when on the profile, featured people (name, title, bio from “Meet …” videos), cities, salary band when published — plus total funding, last round, investors, and founder LinkedIn on profiles that publish them.

When to enable contact enrichment: turn on Person LinkedIn to find when you need a role the company profile did not name (VP Sales, HR). Founder / CEO /in/ from the profile is free. $0.05 only when a paid search finds a profile.

When to enable Lemlist push: add your Lemlist API key + campaign ID to push each lead directly into your outreach sequence — no CSV export needed. Each lead is created with a pre-filled icebreaker (FR or EN) mentioning the open roles and city. A LinkedIn URL is required (person or company). Adds $0.005 per lead pushed.

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What it extracts

Field	Description
company_name	Company name as listed on WTTJ
wttj_url	Direct link to their WTTJ profile
jobs_count_matching	Open roles counted for this company (job-type filter in discover mode; all found jobs in enrich mode)
jobs_matching_titles	List of matching job titles
jobs_count_live	All live jobs on the company profile (every function)
jobs_count_all_functions	Roles in your date / sector / country window, all functions
hiring_focus	Dominant function they are hiring for (Sales, Tech, Marketing…)
hiring_by_function	Count per function — see if they are scaling sales vs product
teams	Published team labels and size on international profiles (not the named people below)
discovery_source	wttj (classic jobs), intel (international jobs board), wttj+intel, or url (pasted company list)
nb_employees	Approximate headcount
size_label	Human-readable size range (e.g. "50–149 employees")
founding_year	Year the company was founded — useful to filter early-stage vs. established
company_summary	One-line pitch from WTTJ — perfect for personalizing cold emails
city	Primary office city
cities	All office cities on matching roles
country_code	ISO country code (FR, GB, US…)
country_codes	All office countries on matching roles
has_remote	True if a matching role is fully remote or hybrid
remote_policies	fulltime / partial / punctual on matching roles
salary_min / salary_max	Yearly salary band when WTTJ published one
salary_currency	Currency of that band
jobs_with_salary	How many matching roles published a salary
jobs_newest_published_at	Date of the newest matching role
labels	Employer labels (great-place-to-work, bcorp, qualiopi…)
equality_index	French gender-equality index when published
benefits	Benefits listed on matching roles
website_url	Company website from the WTTJ profile
company_linkedin_url	Company LinkedIn from the WTTJ profile. A /company/{slug} URL is used only when it matches the company; regional pages are ignored. Free, every run
linkedin_source	wttj, website, or wttj+website
people_linkedin_urls	Public /in/ URLs from the profile, website, or named founders (up to 8)
twitter_url	Twitter / X from the WTTJ profile, when published
total_funding_usd	Published total funding in USD (null when WTTJ has no round)
last_round	Latest round type (SEED, SERIES A–E…)
last_round_amount_usd	Amount of that round, USD
last_round_year	Year of that round
investors	Named investors when listed
founders	Named founders with title and LinkedIn
percentage_female	Female employee share when published
average_age	Average employee age when published
parity_women / parity_men	Gender split when published
published_revenue	Revenue as shown on the profile (e.g. 2.2M)
featured_people	Named people from profile / team videos (name, title, bio). LinkedIn only if WTTJ linked it
employee_growth_pct	Headcount growth over 12 months when published
glassdoor_rating	Glassdoor score when listed
contact_name	Founder / CEO from the profile, a featured “Meet …” person, or paid hop
contact_linkedin_url	Person LinkedIn — free from the profile when published
contact_title	Title on the profile or LinkedIn
contact_certainty	AI confidence 0–100 (paid hop only)
contact_source	public_page, public_profile, or paid_search
lemlist_lead_id	Lemlist lead ID (lea_xxx) if pushed, duplicate if already in campaign (Lemlist ON)
scraped_at	ISO timestamp

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Quick start

1. Open the Actor in Apify Console
2. Either paste WTTJ companies (enky, or the company profile URL) or leave that empty and set Sectors + Job types
3. Contract types — pick Full-time (CDI) or others (discovery only)
4. Countries / cities / remote / salary (optional, discovery only) — UK is GB, US is US. Empty = all markets
5. Published within — set the recency window (default: 3 months; discovery only)
6. Max companies (optional) — cap the number of results or leave at 0 for all matches
7. LinkedIn contact (optional) — choose CEO, CTO, CMO, VP Sales, or HR. $0.05 only when that person's /in/ is found
8. Lemlist (optional) — add your Lemlist API key and campaign ID to push each lead directly into your sequence ($0.005 per lead). Choose FR or EN for the icebreaker language. Works with or without contact enrichment.
9. Click Start — results appear in the Dataset tab as they are collected
10. Export as CSV, JSON, or Excel — or let Lemlist handle the outreach automatically

Ready-made examples (published tasks)

Example	Best for
SaaS companies hiring sales on WTTJ	Cloud / SaaS BD
AI startups hiring engineers on WTTJ	AI / ML talent or vendor outreach
Fintech companies hiring on WTTJ	Payments / insurtech pipeline
E-commerce companies hiring sales on WTTJ	Retail / marketplace BD

LinkedIn enrichment is off in these examples so a Try stays cheap. Turn it on in the input if you need CEO / CTO profiles.

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Input

Parameter	Type	Default	Description
companyUrls	string[]	[]	Company slugs or profile URLs (enky). When not empty, discovery filters are ignored
sectors	string[]	["saas-cloud-services"]	Industry sectors to target (select from 70+ options). Ignored if companyUrls is set
professionCategories	string[]	["Sales & Customer Service"]	Job types to match (20 categories)
contractTypes	string[]	["full_time"]	Contract types to include
countryCodes	string[]	[]	FR, GB, US, DE… Empty = all markets on WTTJ
cities	string[]	[]	Exact office names (Paris, London). Empty = all cities
remotePolicies	string[]	[]	fulltime, partial, punctual. Empty = any
salaryYearlyMin	integer	0	Yearly salary floor. 0 = no salary filter
monthsBack	integer	3	Only include postings from the last N months (1–12)
maxCompanies	integer	0	Max companies to export (0 = no cap)
contactRole	string	""	Role to find: ceo, founder, cto, cmo, vp-sales, or hr. Leave empty to skip. PPE $0.05 only on a found /in/
icebreakerLanguage	string	"en"	Language for the Lemlist icebreaker: en (English) or fr (French)
lemlistApiKey	string	""	Your Lemlist API key (Settings → Integrations). Leave empty to skip Lemlist push
lemlistCampaignId	string	""	Target campaign ID (e.g. cam_A1B2C3). Found in the campaign URL
lemlistFindEmail	boolean	false	Ask Lemlist to find a verified email for each lead (consumes Lemlist credits)
deduplicateAcrossRuns	boolean	false	Skip companies already exported in a previous run — ideal for daily schedules. The seen list is stored in a persistent Apify Key-Value Store (wttj-seen-slugs). Note: when enabled, maxCompanies caps new companies only (full pool is fetched first)
resetDeduplication	boolean	false	Clear the seen companies cache and start fresh on the next run

JSON example — enrich companies you already know

{
  "companyUrls": ["enky", "payfit", "alan"]
}

JSON example — SaaS companies hiring Sales (CDI, last 3 months)

{
  "sectors": ["saas-cloud-services", "software-1"],
  "professionCategories": ["Sales & Customer Service"],
  "contractTypes": ["full_time"],
  "countryCodes": ["FR"],
  "monthsBack": 3,
  "maxCompanies": 0
}

JSON example — FinTech companies hiring Tech, with CEO LinkedIn + Lemlist push (French)

{
  "sectors": ["fintech-insurtech", "banking-1"],
  "professionCategories": ["Tech & Engineering"],
  "contractTypes": ["full_time"],
  "monthsBack": 6,
  "contactRole": "ceo",
  "minCertaintyScore": 75,
  "icebreakerLanguage": "fr",
  "lemlistApiKey": "YOUR_LEMLIST_API_KEY",
  "lemlistCampaignId": "cam_XXXXXXXXXX",
  "lemlistFindEmail": false
}

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How it works

1. Search or enrich — paste slugs (enky) to enrich those profiles, or leave the list empty to query both WTTJ job boards
2. Filter — in discover mode, keeps companies with at least one role matching your job types
3. Deduplication — one row per company. A cap splits classic vs international-only employers so one board does not eat the quota
4. Company profile — we load the public page from the slug (no /team-3 to paste): stats, featured people + bios, jobs, then funding/founders when that profile publishes them
5. Contact enrichment (optional) — searches another role only if you asked and the profile did not already name them
6. Lemlist push (optional) — pushes each lead with a pre-filled FR/EN icebreaker
7. Export — one clean row per company

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Output sample

{
  "company_name": "Enky",
  "wttj_slug": "enky",
  "wttj_url": "https://www.welcometothejungle.com/en/companies/enky",
  "discovery_source": "url",
  "nb_employees": 37,
  "founding_year": 2020,
  "average_age": 30,
  "parity_women": 68,
  "parity_men": 32,
  "published_revenue": "2.2M",
  "featured_people": [
    { "name": "Aïssa", "title": "Founder & CEO", "bio": "With over a decade of experience…" }
  ],
  "contact_name": "Aïssa",
  "contact_title": "Founder & CEO",
  "contact_source": "public_page",
  "website_url": "https://www.enky.com",
  "company_linkedin_url": "https://www.linkedin.com/company/enky",
  "scraped_at": "2026-08-20T12:00:00.000Z"
}

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Performance and cost

Scenario	~Companies	Cost estimate
SaaS + Sales + CDI, last 3 months	~100	$0.30
All Tech sectors, last 6 months, no cap	~500	$1.50
100 companies + CEO LinkedIn enrichment	100	$0.30 + $5.00 = $5.30
100 companies + CEO enrichment + Lemlist push	100	$0.30 + $5.00 + $0.50 = $5.80

Compute costs on Apify platform are additional (minimal — this Actor uses plain HTTP, no browser).

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Local development

Setup

cd wttj-hiring-signal-scraper
npm install

Run locally

$npm start

Edit storage/key_value_stores/default/INPUT.json to adjust filters for local runs.

Run tests

$npm test

109 unit tests covering filters, slug enrichment, profile stats / team people, contact enrichment, and Lemlist.

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Important / limitations

• Daily run deduplication — enable deduplicateAcrossRuns when scheduling the Actor daily. Each run only enriches and pushes companies not seen in previous runs, saving enrichment credits. Lemlist also deduplicates natively (409 on existing leads), so the Lemlist push is safe to run without deduplication too.
• Results depend on what companies have active listings on WTTJ — not all companies use the platform
• The profession category filter on the classic board is applied after pagination; very broad filters may fetch many pages before finding matches
• Named people on WTTJ videos (featured_people) usually have no public /in/ — you get name, title and bio
• International-board discovery uses live jobs (function, location, sector tags). Contract type, salary floor, and “published within N months” apply to the classic board only
• Contact enrichment uses public Google search results — accuracy depends on how prominent the contact is online. The certainty score helps you filter low-confidence results
• LinkedIn URLs may change over time — re-run enrichment periodically for up-to-date profiles

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Plug into your outreach stack

The dataset exports as CSV, JSON, or Excel in one click from the Apify Console.

Option A — Native Lemlist push (zero export needed):

1. Add your Lemlist API key + campaign ID in the input form
2. Run the Actor — each lead is pushed automatically with a pre-filled icebreaker
3. Choose FR or EN for the message language
4. Enable Find email to let Lemlist search for a verified email (uses Lemlist credits)

Option B — Manual CSV import:

1. Run the Actor with enrichment ON
2. Export as CSV from the Dataset tab
3. Import directly into lemlist as a new lead list

Want to add professional emails without Lemlist credits? Run the output through FullEnrich — paste the contact_linkedin_url and get a verified email in seconds.

Full sequence: WTTJ hiring signal → LinkedIn contact → email (FullEnrich) → outreach (lemlist).

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More tools for B2B prospecting

These Actors pair naturally with the WTTJ Hiring Signal scraper:

Actor	How it fits
French Companies · Search & SIREN Enrichment	Enrich any company from the dataset with its SIREN, legal form, NAF code, and official address from the French register
French Legal Announcements · BODACC Scraper	Cross-reference hiring signals with legal events — fundraising, registrations, insolvency — for deeper qualification
Facebook Ads Library Scraper	Check whether a company is actively running Meta ads — another strong budget signal alongside the WTTJ hiring activity
Facebook Page Scraper · Extract Email, Phone & Website	Find the contact email and phone from a company's Facebook page when LinkedIn enrichment isn't enough
Lemlist Usage Detector	Check whether a prospect already uses lemlist — useful for targeting or for avoiding cannibalism in your sequence
HubSpot Email Checker	Verify whether a contact email has an active HubSpot login — qualifies inbound leads before sequencing

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How much does it cost to scrape Welcome to the Jungle?

This Actor uses pay-per-event pricing — you only pay for what you get.

Event	Price	When
wttj-company	$0.003	Each exported company row (discover or enrich). Website, LinkedIn, stats and team when WTTJ publishes them
contact-enrichment	$0.05	Only when a named person /in/ is found (CEO/CTO…). Public /in/ on the page is free. Misses are not billed.
lemlist-push	$0.005	Only when a new lead is created in Lemlist (duplicates/skips free)

Scenario	Approx. cost
10 companies (Try, no paid hop)	~$0.03
100 companies, no person search	~$0.30
100 companies + 80 CEO /in/ found	~$0.30 + $4.00 = ~$4.30
100 companies + 80 CEO + 80 Lemlist pushes	~$4.70
500 companies, no person search	~$1.50

The Actor uses plain HTTP (no browser) — compute costs are minimal. No residential proxy needed.

Is it legal to scrape Welcome to the Jungle?

This Actor only accesses data that Welcome to the Jungle makes publicly available on its website — company names, office locations, headcount, and active job listings visible to any visitor without a login. No authentication is bypassed and no personal data is collected beyond what is displayed publicly on the platform. As with any data involving companies or individuals, ensure your use complies with GDPR and relevant regulations in your jurisdiction.

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Support

Questions or custom scraping needs? Contact corentin@outreacher.fr