Welcome to the Jungle (WTTJ) Jobs Scraper

💼 Welcome to the Jungle (WTTJ) Jobs Scraper – Job Listings, Salary & Company Data Export

The Welcome to the Jungle jobs scraper extracts job listings from welcometothejungle.com and returns them as structured records containing the role, contract type, salary range, remote policy, required skills, full job description and a detailed company profile. Search by keyword and location, apply the same filters you would use on the site itself — contract type, experience level, remote policy, company size, industry sector, minimum salary — and export the results as clean job data ready for analysis, aggregation or an ATS import.

Welcome to the Jungle is one of the richest job boards in the European market, particularly across France, and its listings carry far more employer context than a typical aggregator. This Welcome to the Jungle jobs scraper preserves that depth. Alongside the usual title, location and contract fields you get the company's employee count, founding year, gender parity figures, average employee age, social profiles, commitments and labels, plus the recruitment process and ATS application questions attached to each posting.

The includeDetails switch controls how deep the scraper goes. Left on, it queries each job's details endpoint to pull full descriptions, candidate profile requirements, tools, benefits and key missions. Turned off, it performs an ultra-fast shallow sweep that is ideal for high-volume listing counts and change monitoring.

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📊 What Data Can You Extract with This Welcome to the Jungle Jobs Scraper?

The scraper returns one record per job listing, combining the posting itself with a substantial employer profile.

Category	Fields	What you get
🧾 Job identity	id, reference, name, url, applyUrl, publishedAt, startDate	The listing identifier and reference, the job title, its page URL, the direct application link and its publication and start dates
📑 Role classification	contractType, remote, language, experienceLevel, educationLevel, category, subcategory, sectors, contractDurationMin, contractDurationMax	Contract type and duration, remote policy, listing language, required experience and education, and the category taxonomy
💰 Compensation	salaryMin, salaryMax, salaryCurrency, salaryPeriod	The advertised salary range with its currency and reference period, where the employer discloses it
📝 Job content	summary, description, profile, keyMissions, skills, tools, benefits, team, recruitmentProcess	The short summary and full description, the candidate profile sought, key missions, required skills and tools, benefits, team context and the hiring process
🏢 Company profile	organizationName, organizationSlug, organizationLogo, organizationDescription, companySummary, organizationCoverImage, organizationIndustry, organizationWebsite, organizationJobsCount	The employer's name, identifiers, branding assets, description, industry and how many roles they currently list
📊 Company metrics	organizationEmployees, organizationCreationYear, organizationRevenue, organizationTurnover, organizationAverageAge, organizationParityMen, organizationParityWomen, organizationLabels, organizationCommitments, organizationHeadquarter	Headcount, founding year, revenue and turnover figures, workforce demographics, certifications and headquarters
🔗 Employer channels & ATS	organizationLinkedin, organizationTwitter, organizationFacebook, organizationInstagram, organizationHasExternalAts, organizationAutomaticEmail, organizationGdprSetting, atsQuestions, applicationFields, offices	Social profiles, ATS configuration, GDPR settings, application questions and office locations

The most commercially valuable combination here is salaryMin, salaryMax and salaryCurrency alongside organizationEmployees and experienceLevel. Very few job boards publish structured salary data at all, and having it linked to company size and seniority turns a job feed into a genuine compensation benchmarking dataset.

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🌟 Key Features of the Welcome to the Jungle Jobs Scraper

Feature	Description
🔍 Keyword and location search	Search by job title or keywords and target a city, country or "Worldwide", with a configurable radius in kilometres
🎛️ Full native filter set	Contract type, remote policy, experience level, company size, job language, industry sector, job category and minimum yearly salary
💰 Structured salary fields	Salary minimum, maximum, currency and period are returned as separate fields rather than buried in free text
🏢 Deep employer profiles	Headcount, founding year, revenue, turnover, parity figures, average age, labels and commitments accompany every listing
⚡ Shallow or deep mode	includeDetails toggles between an ultra-fast listing sweep and a full fetch of descriptions, tools, benefits and profiles
🎯 Title-only matching	searchOnlyInJobTitle restricts matches strictly to job titles, cutting out roles that merely mention your keyword in the body
🗓️ Recency filtering	datePosted narrows results to the past 24 hours, 48 hours, 7 days or 30 days for fresh-listing monitoring
🌐 Language scoping	Filter listings by website country code and by job language across French, English, German, Spanish, Italian, Portuguese, Czech and Dutch
🔗 Direct apply links	applyUrl provides the direct application destination, with organizationHasExternalAts flagging externally hosted ATS flows

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🚀 Why Choose This Welcome to the Jungle Jobs Scraper?

Structured salary data, not a text blob. Compensation is the hardest field to extract reliably from most job boards because it is usually free text. Here salaryMin, salaryMax, salaryCurrency and salaryPeriod arrive as discrete fields, which means salary benchmarking works out of the box rather than requiring a parsing layer.

Employer intelligence that other scrapers discard. organizationEmployees, organizationCreationYear, organizationParityMen, organizationParityWomen, organizationAverageAge and organizationCommitments describe what a company is actually like. For recruitment marketing, employer-brand research and candidate-facing tools, this context is often more valuable than the job posting itself.

A real speed-versus-depth choice. includeDetails is not a cosmetic setting. With it off you get fast, cheap listing sweeps suitable for monitoring thousands of roles daily. With it on you get the full description, profile, keyMissions, tools and benefits for genuine content analysis. You choose per run.

Filtering that mirrors the site. Because the scraper exposes the same filters candidates use — contract type, remote policy, experience level, company size, sector, language, minimum salary — you can reproduce a precise on-site search programmatically instead of collecting everything and filtering afterwards.

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📥 Input

Every field is optional; running with defaults searches broadly with full details enabled.

{
  "query": "React Developer",
  "location": "Paris, France",
  "locationRadius": "20",
  "searchOnlyInJobTitle": false,
  "websiteCountry": "en",
  "sortBy": "relevance",
  "datePosted": "any",
  "contractType": "",
  "remoteWork": "",
  "experienceLevel": "",
  "companySize": "",
  "jobLanguage": "",
  "maxItems": 100,
  "includeDetails": true
}

🔧 Welcome to the Jungle Jobs Scraper Input Fields

Field	Type	Required	Default	Description
query	string	No	prefilled React Developer	Keywords to search for, such as "React Developer" or "Product Designer". Leave blank to search all jobs.
location	string	No	Worldwide	Location to search in, for example "Paris, France" or "London". Use "Worldwide" for global listings.
locationRadius	string	No	20	Distance radius in kilometres around the specified location.
searchOnlyInJobTitle	boolean	No	false	If enabled, restricts search matches strictly to job titles.
websiteCountry	string	No	en	Language or country code used to filter listings, for example en, fr, es, cs, sk. Use any to disable filtering.
sortBy	string	No	relevance	Sort order of job listings. One of relevance or newest.
datePosted	string	No	any	Time range of job publication. One of any, today, yesterday, past_week, past_month.
contractType	string	No	""	Narrow results by contract type: permanent, temporary, internship, work_study, freelance, icvp, other, part_time.
remoteWork	string	No	""	Narrow results by remote policy: no_remote, partial, occasional, full, unknown.
experienceLevel	string	No	""	Narrow results by experience: less_than_1_year, 1_to_3_years, 3_to_5_years, 5_to_10_years, more_than_10_years.
minimumYearlySalary	integer	No	—	Narrow results by minimum yearly salary.
companySize	string	No	""	Narrow results by headcount band: 1_to_15, 15_to_50, 50_to_250, 250_to_2000, more_than_2000.
jobLanguage	string	No	""	Narrow results by job language: fr, en, de, es, it, pt, cs, nl.
industrySector	string	No	—	Narrow results by industry sector.
jobCategory	string	No	—	Narrow results by job category.
maxItems	integer	No	100	Maximum number of job listings to scrape.
includeDetails	boolean	No	true	If enabled, fetches full descriptions, profile texts, tools, benefits and video links from each job's details API. Turn off for ultra-fast, shallow scrapes.

💡 Input Examples

Fresh remote engineering roles in France

{
  "query": "backend engineer",
  "location": "Paris, France",
  "locationRadius": "50",
  "remoteWork": "full",
  "datePosted": "past_week",
  "sortBy": "newest",
  "maxItems": 200
}

Salary benchmarking for senior product roles

{
  "query": "product manager",
  "location": "Worldwide",
  "experienceLevel": "5_to_10_years",
  "minimumYearlySalary": 60000,
  "contractType": "permanent",
  "maxItems": 500,
  "includeDetails": true
}

Fast shallow sweep for daily monitoring

{
  "query": "",
  "location": "Lyon, France",
  "companySize": "50_to_250",
  "datePosted": "today",
  "maxItems": 1000,
  "includeDetails": false
}

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📤 Output

Each dataset item is one job listing with its employer profile attached. The example below uses the real output field names with representative values, trimmed for readability.

{
  "id": "8f2a1c44-91de-4f0b-b0a7-3c9de2f4a112",
  "reference": "WTTJ-2026-08-4412",
  "name": "Senior React Developer",
  "url": "https://www.welcometothejungle.com/en/companies/example-tech/jobs/senior-react-developer",
  "applyUrl": "https://www.welcometothejungle.com/en/companies/example-tech/jobs/senior-react-developer/apply",
  "contractType": "permanent",
  "remote": "partial",
  "language": "en",
  "salaryMin": 55000,
  "salaryMax": 70000,
  "salaryCurrency": "EUR",
  "salaryPeriod": "yearly",
  "experienceLevel": "3_to_5_years",
  "educationLevel": "master",
  "publishedAt": "2026-08-11T09:24:00Z",
  "startDate": "2026-10-01",
  "category": "Tech",
  "subcategory": "Frontend Development",
  "summary": "Join a product team building a design-system-driven React application used by 40,000 daily users.",
  "skills": ["React", "TypeScript", "GraphQL", "Testing Library"],
  "benefits": ["Meal vouchers", "Health insurance", "Two remote days per week"],
  "organizationName": "Example Tech",
  "organizationEmployees": 180,
  "organizationCreationYear": 2016,
  "organizationWebsite": "https://example-tech.com",
  "organizationJobsCount": 14
}

🧾 Welcome to the Jungle Job Output Fields

Field	Type	Description
id	string | integer	Unique identifier of the job listing.
reference	—	Reference code of the listing.
name	string | null	Job title.
url	string | null	Canonical URL of the scraped listing.
applyUrl	string | null	URL to apply for this job.
contractType	string | null	Contract type of the job.
remote	—	Whether and to what extent the job is remote.
language	string | null	Language of the listing content.
salaryMin	—	Lower bound of the advertised salary range.
salaryMax	—	Upper bound of the advertised salary range.
salaryCurrency	—	Currency of the advertised salary.
salaryPeriod	—	Period the salary refers to.
experienceLevel	—	Required experience level.
educationLevel	—	Required education level.
contractDurationMin	—	Minimum contract duration.
contractDurationMax	—	Maximum contract duration.
publishedAt	string | null	When the listing was published.
startDate	string | null	Start date of the role.
category	string | null	Category of the job.
subcategory	string | null	Sub-category of the job.
sectors	—	Sectors associated with the listing.
summary	string | null	Short summary of the job.
description	string | null	Long-form job description text.
profile	string | null	Candidate profile sought for the role.
keyMissions	—	Key missions of the role.
skills	array | null	Skills required or listed.
tools	—	Tools used in the role.
benefits	array | null	Benefits offered.
team	—	Team context for the role.
recruitmentProcess	—	Description of the recruitment process.
offices	—	Office locations associated with the listing.
atsQuestions	—	ATS questions attached to the application.
applicationFields	—	Application fields required by the employer.

🧾 Company & Employer Output Fields

Field	Type	Description
organizationName	string | null	Name of the hiring organisation.
organizationSlug	—	Organisation slug used in URLs.
organizationLogo	—	Organisation logo asset.
organizationCoverImage	—	Organisation cover image asset.
organizationDescription	string | null	Description of the organisation.
companySummary	—	Short company summary.
organizationIndustry	—	Industry the organisation operates in.
organizationWebsite	string | null	Organisation website.
organizationEmployees	—	Number of employees at the organisation.
organizationCreationYear	—	Year the organisation was founded.
organizationRevenue	—	Reported organisation revenue.
organizationTurnover	—	Reported organisation turnover.
organizationAverageAge	—	Average age of employees.
organizationParityMen	—	Reported share of men in the workforce.
organizationParityWomen	—	Reported share of women in the workforce.
organizationLabels	—	Labels and certifications held by the organisation.
organizationCommitments	—	Public commitments made by the organisation.
organizationHeadquarter	—	Headquarters location.
organizationJobsCount	integer | null	Number of jobs the organisation currently lists.
organizationLinkedin	—	Organisation LinkedIn profile.
organizationTwitter	—	Organisation Twitter/X profile.
organizationFacebook	—	Organisation Facebook profile.
organizationInstagram	—	Organisation Instagram profile.
organizationHasExternalAts	—	Whether the organisation uses an external applicant tracking system.
organizationAutomaticEmail	—	Whether automatic application emails are configured.
organizationGdprSetting	—	GDPR setting configured by the organisation.

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💻 How to Use the Welcome to the Jungle Jobs Scraper (Step by Step)

Step 1: Define Your Job Search Query

query accepts keywords or a job title such as "React Developer" or "Product Designer". Leaving it blank searches all jobs, which is the right choice when you want complete coverage of a location or company-size band rather than a specific role. If your keyword is a common word that appears in many descriptions — "data", "product", "growth" — consider enabling searchOnlyInJobTitle so matches are restricted to titles and you are not swamped by roles that merely mention the term in passing.

Step 2: Set Location and Radius

location defaults to "Worldwide" and accepts natural strings like "Paris, France" or "London". locationRadius controls how far around that point results may fall, in kilometres, defaulting to 20. For a dense metropolitan area, 20 to 30 kilometres captures the practical commuting zone. For regional coverage, widen it to 50 or more. If you are specifically after remote roles, pair a wide radius with remoteWork set to full rather than relying on geography alone.

Step 3: Apply the Filters That Match Your Brief

This is where the scraper earns its keep. contractType separates permanent roles from internships, freelance work and work-study contracts. experienceLevel bands seniority. companySize distinguishes early-stage startups from large enterprises. minimumYearlySalary removes listings below your threshold. jobLanguage and websiteCountry control the linguistic scope, which matters a great deal on a board with strong French and English coverage. Applying these at collection time is far more efficient than scraping everything and filtering afterwards.

Step 4: Choose Sort Order and Recency

sortBy accepts relevance or newest. Relevance is the better default for research, where you want the strongest matches. Newest is what you want for monitoring, where the goal is catching listings as they appear. Pair it with datePosted — today for a daily monitoring job, past_week for a weekly digest, past_month for a broader recruitment-market snapshot. Using newest with any returns fresh listings but does not restrict how far back the dataset reaches.

Step 5: Decide Between Shallow and Detailed Mode

includeDetails defaults to true, which fetches each job's details endpoint to populate description, profile, keyMissions, tools, benefits and related fields. That is what you want for content analysis, skills extraction or building a candidate-facing product. Turn it off when you are running a high-volume sweep for monitoring or counting purposes — the shallow mode is dramatically faster and still returns titles, salaries, contract types and company names.

Step 6: Set maxItems and Run the Scraper

maxItems defaults to 100. Start there to confirm your filters return the roles you expect, then scale. Launch from the Apify Console or through the API and watch the log as listings are collected. If early results are dominated by roles you did not intend to capture, stop the run and tighten searchOnlyInJobTitle, jobCategory or contractType rather than letting a large job complete with the wrong data.

Step 7: Analyse and Export the Job Data

Export as CSV or Excel for spreadsheet analysis, or pull JSON through the dataset API into a notebook or database. For salary work, filter to records where salaryMin and salaryMax are populated and group by experienceLevel, category or organizationEmployees. For skills analysis, explode the skills array and count frequencies. For employer research, de-duplicate on organizationName and keep the company-level fields as a separate table.

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🔌 API Access & Integrations

Run the Welcome to the Jungle jobs scraper and collect results in one call:

curl -X POST "https://api.apify.com/v2/acts/scrapers-hub~wttj-jobs-scraper/run-sync-get-dataset-items?token=YOUR_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "query": "React Developer",
    "location": "Paris, France",
    "locationRadius": "30",
    "contractType": "permanent",
    "remoteWork": "partial",
    "datePosted": "past_week",
    "sortBy": "newest",
    "maxItems": 200,
    "includeDetails": true
  }'

The same run in Python, producing a quick salary summary:

import statistics
from apify_client import ApifyClient

client = ApifyClient("YOUR_TOKEN")

run_input = {
    "query": "product manager",
    "location": "Worldwide",
    "experienceLevel": "5_to_10_years",
    "contractType": "permanent",
    "maxItems": 500,
    "includeDetails": True,
}

run = client.actor("scrapers-hub/wttj-jobs-scraper").call(run_input=run_input)

salaries = []
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    lo, hi = item.get("salaryMin"), item.get("salaryMax")
    if lo and hi:
        salaries.append((lo + hi) / 2)
    print(item.get("name"), "|", item.get("organizationName"), "|", item.get("contractType"))

if salaries:
    print("Median advertised midpoint:", statistics.median(salaries))

The Actor also connects to Zapier, Make, Google Sheets, Slack and any custom endpoint via Apify webhooks, so a scheduled run can push new listings into a spreadsheet, an ATS or a hiring channel automatically.

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💡 Best Use Cases for Welcome to the Jungle Jobs Data

💰 Salary Benchmarking and Compensation Research

Because salaryMin, salaryMax, salaryCurrency and salaryPeriod are structured fields, you can build genuine compensation benchmarks rather than estimates. Segment by experienceLevel, category and organizationEmployees to see how advertised pay moves with seniority, function and company size across the market.

🧭 Job Board Aggregation and Vertical Search

The combination of name, url, applyUrl, contractType, remote and publishedAt gives you everything needed to populate a niche job board or vertical search product. organizationHasExternalAts tells you whether an application will hand off to a third-party ATS, which matters when designing an apply flow.

📈 Hiring Trend and Labour Market Analysis

Scraping on a schedule and tracking listing volume by category, subcategory and sectors over time reveals which functions are expanding and which are contracting. publishedAt provides the time axis, and remote shows how remote-work policy is shifting across a market.

🛠️ Skills Demand and Curriculum Planning

Exploding the skills and tools arrays across a large sample produces a frequency ranking of what employers actually ask for. Training providers, bootcamps and internal L&D teams use exactly this signal to decide what to teach, and experienceLevel shows at which career stage each skill becomes a requirement.

🏢 Employer Brand and Competitor Research

organizationEmployees, organizationCreationYear, organizationParityMen, organizationParityWomen, organizationAverageAge, organizationLabels and organizationCommitments form a rich employer-brand dataset. Recruitment marketers use it to see how competitors present themselves and which commitments are becoming table stakes in a sector.

🎯 Recruitment Lead Generation

organizationJobsCount identifies companies hiring at volume — the best possible prospects for a recruitment agency or HR software vendor. Pair it with organizationWebsite, organizationLinkedin and organizationHeadquarter to build an outreach list of actively growing employers.

🤖 Candidate Matching and Job Alert Products

With description, profile, keyMissions, skills and benefits populated in detailed mode, each listing becomes a document suitable for semantic matching against candidate profiles. applyUrl closes the loop by giving matched candidates a direct route to apply.

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⚙️ Tips for Better Welcome to the Jungle Jobs Scraping Results

• Turn off includeDetails for volume runs. Shallow mode is far faster and still returns titles, salaries, contract types and company names — plenty for monitoring and counting work.
• Enable searchOnlyInJobTitle for generic keywords. Terms like "data", "product" or "growth" appear in countless descriptions; restricting to titles sharpens results dramatically.
• Set websiteCountry and jobLanguage deliberately. The board carries strong French and English coverage; leaving language filtering at the default can silently exclude a large part of the market you care about.
• Pair sortBy: newest with a datePosted window. Sorting by newest alone does not bound how far back the dataset reaches; combining it with today or past_week is what makes a monitoring run efficient.
• Widen locationRadius before broadening query. For regional coverage, increasing the radius keeps role relevance intact, whereas loosening the keyword introduces roles you did not want.
• Treat salary fields as optional. Not every employer discloses pay, so filter to records where salaryMin and salaryMax are populated before running any compensation analysis.

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🛠️ Troubleshooting

Why are salary fields empty on many listings? Employers are not obliged to publish compensation, so salaryMin, salaryMax, salaryCurrency and salaryPeriod are only populated when the listing discloses them. For benchmarking, filter to records where they are present rather than treating nulls as zero.

Why is description empty even though the job clearly has one? includeDetails was almost certainly set to false. Shallow mode deliberately skips the per-job details request, which is what makes it fast. Re-run with includeDetails enabled to populate description, profile, keyMissions, tools and benefits.

The run returned fewer listings than maxItems. Your filters have narrowed the result set below that number. Combining a narrow query, a tight locationRadius, a specific contractType and a datePosted window can easily leave only a handful of matching roles. Relax one filter at a time to see which is the binding constraint.

My results include roles that do not match my keyword. The search matches across listing content by default. Set searchOnlyInJobTitle to true to restrict matching strictly to job titles, and consider using jobCategory to constrain the functional area as well.

Runs with details enabled are slow on large maxItems values. Detailed mode issues an additional request per listing, so runtime scales with listing count. Either reduce maxItems, or split the work into several narrower runs by location or category so results become available incrementally.

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❓ Frequently Asked Questions About Welcome to the Jungle Jobs Scraping

What does the Welcome to the Jungle jobs scraper do? It searches welcometothejungle.com for job listings matching your query and filters, then returns each listing as a structured record with role details, salary fields, skills, description and a full company profile.

Do I need a Welcome to the Jungle account? No. The scraper works with publicly accessible job listings and does not require login credentials.

Can I search all jobs without a keyword? Yes. Leave query blank and the scraper searches all jobs, which is the right approach when your filter is geographic, sector-based or company-size-based rather than role-based.

How do I search a specific city? Set location to a natural string such as "Paris, France" and adjust locationRadius to control how far around it results may fall. Use "Worldwide" for global coverage.

Which contract types can I filter by? Permanent, temporary, internship, work-study, freelance, international corporate volunteer programme, part-time and other.

Can I filter for remote jobs? Yes. remoteWork accepts no remote work, partial, occasional, fully remote and unknown, and the resulting policy is returned in the remote output field.

Does the scraper return salary information? Yes, where the employer publishes it. Salary arrives as four separate fields — salaryMin, salaryMax, salaryCurrency and salaryPeriod — rather than embedded in free text.

Can I set a minimum salary threshold? Yes. minimumYearlySalary filters out listings advertising below the value you specify.

What is the difference between shallow and detailed mode? With includeDetails off, the scraper collects listing-level data quickly. With it on, it additionally queries each job's details API for full descriptions, candidate profiles, tools, benefits and video links.

What company information is included with each job? A substantial profile: name, website, industry, headcount, founding year, revenue and turnover, average employee age, gender parity figures, labels, commitments, headquarters, social profiles and current jobs count.

Can I filter by company size? Yes. companySize accepts bands of 1-15, 15-50, 50-250, 250-2000 and 2000+ employees.

How do I get only recently posted jobs? Set datePosted to today, yesterday, past_week or past_month, and pair it with sortBy: newest for monitoring workflows.

Which languages are supported for filtering? jobLanguage covers French, English, German, Spanish, Italian, Portuguese, Czech and Dutch, while websiteCountry filters listings by language or country code and accepts any to disable filtering.

Can I schedule the Welcome to the Jungle jobs scraper? Yes. Apify Schedules run it on a cron expression, which is the standard setup for daily new-listing monitoring and weekly hiring-trend snapshots.

What export formats are available? Apify datasets export as JSON, JSONL, CSV, Excel, XML and RSS from the Console, and are also accessible through the dataset API for direct integration.

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🆘 Support & Feedback

Something not behaving as expected? Report it on the Issues tab of the Actor page, including your input configuration and the run ID so the problem can be reproduced quickly.

Need a custom build? For extra fields, different filter behaviour, higher-volume scheduling or direct integration with your ATS or data warehouse, get in touch at scraperhubapi@gmail.com with a short description of what you need.

If the Welcome to the Jungle jobs scraper is useful to you, please leave a review on the Apify Store. Reviews help other recruiters and analysts find the tool and guide what gets improved next.

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⚖️ Disclaimer

This Welcome to the Jungle jobs scraper collects publicly available job listings and the company information published alongside them. It does not access private candidate data, log into accounts, or bypass any authentication or access control.

You are responsible for how the collected job data is used. While job listings are largely corporate rather than personal information, records may still include named contacts, recruiter details or other personal data. Where that applies, obligations arise under the GDPR, the CCPA and comparable privacy regimes: establish a lawful basis, minimise what you retain, secure it appropriately and delete it when it is no longer needed.

Respect the terms of service of welcometothejungle.com and any employer site you follow through to, along with applicable rate limits. Job descriptions and employer branding assets remain the intellectual property of their owners; republishing them verbatim may require permission, and using the data to spam employers or candidates is not acceptable.

If you would like data relating to you or your organisation removed from a dataset produced by this Actor, contact scraperhubapi@gmail.com and the request will be handled promptly.