XING Jobs Scraper & API - job ads from Germany, Austria and Switzerland (salaries, career level, remote)

XING Jobs Scraper is a XING scraper and unofficial XING jobs API for the DACH job market. Give it keywords, cities and filters (or paste XING search, landing-page or job URLs) and get every matching job ad on www.xing.com/jobs as clean JSON, CSV or Excel: title, company (with size, industry and XING page), location down to the postal code, salary (stated in the ad or XING's own estimate), full-time / part-time / freelance, career level, job field, industry, remote / hybrid / on-site, posting and expiry dates, the apply link and, if you want it, the full job description.

It reads the same JSON the XING website loads, over plain HTTP: no browser, no login and no XING account. XING returns at most 1,000 jobs for any one search; this scraper splits big searches automatically by job field, career level, employment type and remote option and merges the results without duplicates. In testing, every job in Hamburg (22,107 listed) returned 21,888 unique jobs (99%) in 40 seconds.

Use it for recruiting and staffing lead generation in Germany, Austria and Switzerland (which companies are hiring SAP consultants or nurses in Munich this week), job boards and aggregators, salary benchmarking in EUR and CHF, sales prospecting on hiring signals, and DACH labour-market research.

How to use

1. Type one or more Search keywords (software engineer, Pflegefachkraft, Vertrieb, SAP) and, optionally, Cities (Berlin, München, Hamburg, Wien, Zürich). Every keyword is searched in every city. No keyword = all jobs matching your cities and filters.
2. Optionally filter: Remote / hybrid / on-site, Employment types, Career levels, Job fields, Industries, Posted within (days), Minimum yearly salary, Countries, Radius. Tick Exact keyword matches only if you want only ads that contain your keyword (XING matches by meaning).
3. Set Max jobs per search (0 = everything). Tick Include full description if you need the ad text.
4. Click Start. Download the results as JSON, CSV, Excel or HTML from the Output tab, or pull them through the Apify API, Make, Zapier, n8n or an MCP client.

Already have links? Paste XING search URLs, landing pages (https://www.xing.com/jobs/t-python, https://www.xing.com/jobs/l-berlin) or job URLs into XING URLs instead; the filters in a search URL are read from the URL.

What you get

One dataset item per job ad:

• Identity: id (XING job id, stable across runs), xingId, title, url.
• Company: companyName, companyId, companyUrl (XING company page), companyLogoUrl, companySize ("51-200 employees"), companyIndustry, companyCity, companyCountry.
• Where: location, city, postalCode, region, street (workplace address when given), countryCode, country, additionalLocations, isRemote, workplaceTypes (Remote, Hybrid, On-site).
• Work: employmentType (Full-time, Part-time, Self-employed, Student, Temp, ...), careerLevel (Entry Level ... Senior Executive), discipline (XING's 22 job fields), industry.
• Pay: salaryMin, salaryMax, salaryMedian, salaryCurrency (EUR, CHF), salaryPeriod (year) and salarySource: employer when the ad states the pay, xing-estimate when it is XING's estimate.
• Dates: postedAt, refreshedAt, expiresAt.
• Applying: applyType (external, xing), applyUrl (the employer's application page), redirectsToExternalSite.
• Extras: isPaidAd, isTopJob, adLanguage (de / en / ...), jobCode (the employer's reference), keywords, highlights (XING's short summary of the main tasks).
• Where it came from: sourceQuery, sourceLocation, position, scrapedAt.

With Include full description on, each job also gets description (plain text), optional descriptionHtml, introduction and qualifications (XING's summary), descriptionUrl (for ads hosted elsewhere), companyDescription, companyWebsite and companyKununuUrl (employer reviews). It comes in the same request, so it is just as fast and costs the same.

Use cases

• Staffing and recruiting lead generation (DACH): a company posting three SAP roles in Munich needs people now. Run daily with Posted within 1 day and send new ads to your CRM with the company size, XING page and apply link attached.
• Sales prospecting on hiring signals: companies hiring their first "Head of Data" or ten warehouse workers are buying tools and services. Filter by job field, career level and industry.
• Salary benchmarking: salaryMin / salaryMax by job field, career level, city and country, in EUR and CHF, with salarySource telling stated pay from XING estimates.
• Job boards and aggregators: pull a job field or a whole city into your own feed, deduplicated by id.
• Labour-market research: demand by job field, remote share, freelance share, German vs English ads, over time.
• AI agents: "find hybrid Python jobs in Berlin posted this week paying over 70,000 EUR" maps straight onto the input fields.

Input example

Field	Default	Description
searchQueries	-	Keywords, one search each. Empty = all jobs matching the filters
strictKeywordMatch	false	Keep only ads whose title or XING keywords contain the keyword
locations	all of XING	Cities and towns: Berlin, München, Wien, Zürich
radius	20	Kilometres around each city; 0 = the city only
countries	all	Any of DE, AT, CH, LU (applied after the search)
postedWithinDays	0 (any)	Published or refreshed in the last N days
workplaceTypes	all	Any of remote, hybrid, onsite
employmentTypes	all	Any of fulltime, parttime, freelance, student, seasonal, temporary, volunteer
careerLevels	all	Any of student, entry, professional, manager, director, executive
disciplines	all	XING's 22 job fields, e.g. it-software, sales, health-social, finance-accounting
industries	all	XING's 23 industry groups, e.g. internet-it, banking-finance, automotive
salaryMin / salaryMax	0	Yearly salary range (stated or estimated)
language	en	Labels in English or German (de)
maxItems	100	Max jobs per search; 0 = all
deepSearch	true	Split searches above 500 jobs so nothing is cut off at 1,000
includeDetails	false	Full description, summary, company description and website (no extra requests)
includeDescriptionHtml	false	Also keep the description HTML
startUrls	-	XING search URLs, landing pages or job URLs
proxyConfiguration	Apify datacenter	No residential proxies needed

Experienced Python and data jobs in Munich and Vienna, hybrid or remote, posted in the last two weeks, at least 60,000 a year:

{
    "searchQueries": ["python", "data engineer"],
    "locations": ["München", "Wien"],
    "workplaceTypes": ["remote", "hybrid"],
    "careerLevels": ["professional", "manager"],
    "postedWithinDays": 14,
    "salaryMin": 60000,
    "maxItems": 200,
    "includeDetails": true
}

Every job in Hamburg, no cap:

{
    "locations": ["Hamburg"],
    "maxItems": 0
}

Every sales job in Vienna, Zurich and Graz posted this week, labels in German:

{
    "disciplines": ["sales"],
    "locations": ["Wien", "Zürich", "Graz"],
    "postedWithinDays": 7,
    "language": "de",
    "maxItems": 0
}

From links you copied off the site:

{
    "startUrls": [
        "https://www.xing.com/jobs/search?keywords=python&location=Berlin&employmentType=FULL_TIME.ef2fe9",
        "https://www.xing.com/jobs/t-controller",
        "https://www.xing.com/jobs/berlin-senior-software-engineer-157369813"
    ]
}

Output example

From a real run (search for data scientist in Zürich, details on; long texts shortened):

{
    "id": "158550594",
    "xingId": "158550594.a0c65f",
    "title": "Cloud Data Solutions Specialist (m/w/d)",
    "url": "https://www.xing.com/jobs/zuerich-cloud-data-solutions-specialist-158550594",
    "companyName": "ROCKEN",
    "companyId": "2025879.f36256",
    "companyUrl": "https://www.xing.com/pages/rocken-ag",
    "companyLogoUrl": "https://www.xing.com/imagecache/public/scaled_original_image/eyJ1dWlkIjoiZjJhMWY4NGUt...",
    "companySize": "51-200 employees",
    "companyIndustry": "HR services and consulting",
    "companyCity": "Zürich",
    "companyCountry": "Switzerland",
    "location": "Zürich, Kanton Zürich, Switzerland",
    "city": "Zürich",
    "postalCode": "8001",
    "region": "Kanton Zürich",
    "street": "Zürich",
    "countryCode": "CH",
    "country": "Switzerland",
    "additionalLocations": [],
    "isRemote": false,
    "workplaceTypes": ["Hybrid"],
    "employmentType": "Full-time",
    "careerLevel": "Professional/Experienced",
    "discipline": "IT and software development",
    "industry": "Internet and IT",
    "salaryMin": 102000,
    "salaryMax": 131500,
    "salaryMedian": 115000,
    "salaryCurrency": "CHF",
    "salaryPeriod": "year",
    "salarySource": "xing-estimate",
    "postedAt": "2026-10-14T11:13:15.000Z",
    "refreshedAt": "2026-10-14T11:13:15.000Z",
    "expiresAt": "2026-11-13T11:15:52.000Z",
    "applyType": "external",
    "applyUrl": "https://rocken.jobs/application/APq4VyNdD7Q_cloud-data-solutions-specialist?utm_source=xing&utm_medium=cpc&utm_campaign=sponsored",
    "redirectsToExternalSite": false,
    "isPaidAd": true,
    "isTopJob": false,
    "adLanguage": "de",
    "jobCode": "550312",
    "keywords": ["engineer", "ci", "cd", "etl", "sql", "mysql", "python", "bim", "rocken", "zürich"],
    "highlights": [
        "Entwicklung der unternehmensweiten Datenumgebung.",
        "Entwurf von Datenmodellen und -architekturen.",
        "Automatisierung der Datenverarbeitung durch Pipelines."
    ],
    "sourceQuery": "data scientist",
    "sourceLocation": "Zürich",
    "position": 13,
    "scrapedAt": "2026-10-15T17:36:44.132Z",
    "introduction": "Ein Rocken Partner sucht einen Data Engineer in Zürich. Das Unternehmen entwickelt innovative Immobilienlösungen mit Fokus auf Nachhaltigkeit und Effizienz.",
    "qualifications": ["Studium in Informatik oder Data Science.", "Erfahrung mit ETL-/ELT-Verfahren erforderlich.", "Kenntnisse in SQL und Python notwendig."],
    "description": "Wie entstehen Immobilienprojekte, die Regionen nachhaltig prägen? Unser Rocken Partner entwickelt und realisiert wegweisende Immobilienlösungen – von der ersten Idee bis zur finale ...",
    "descriptionUrl": null,
    "companyDescription": null,
    "companyWebsite": "https://rocken.jobs/",
    "companyKununuUrl": "https://www.kununu.com/ch/rocken2"
}

Fill rates on a 21,888-job test run (every job in Hamburg): id, title, URL, company name, location, city, country, employment type, career level, job field, industry, workplace type, dates, apply type, ad language and keywords 100%; region 99%; company page details (id, logo, industry) 82%, company size 69%; salary 81% (20% stated by the employer, 61% XING estimates); external apply link 59%; employer reference code 64%; postal code 43%; XING highlights 37%; street 28%. On a 160-job run with details: description 100%; XING introduction and qualifications 22%; company website 19%; kununu link 6%. A field XING does not publish for a job is null, never guessed.

Pricing

Pay per result: $0.60 per 1,000 jobs (one result = one job ad saved to the dataset). The full description is included in that price.

• 100 jobs for a quick search = $0.06
• Every job in Hamburg, about 21,900 jobs = $13.13
• A daily run of 10 keywords, new jobs only (about 500 jobs) = $0.30 a day

No start fee and no charge per search. Failed requests, duplicate ads, expired job URLs, empty searches and jobs skipped by the country or exact-keyword filters are never charged. If you set a maximum cost per run, the scraper stops cleanly when it reaches it. Apify's free plan includes $5 of monthly platform credit, enough for about 8,000 jobs.

Integrations

• Apify API: run the actor and fetch results over REST, or with the official Python and JavaScript / Node.js clients.
• MCP for AI agents: through the Apify MCP server (https://mcp.apify.com), Claude, ChatGPT, Cursor and other agents can call this XING jobs API directly and read the results. The input fields are described for agents.
• Make, Zapier and n8n: start runs and push new jobs to your CRM, Slack, Google Sheets or Airtable.
• Webhooks: get notified when a run finishes and process the data right away.
• Schedules: run daily with postedWithinDays: 1 and deduplicate by id to collect only new ads.
• Google Sheets: export the dataset straight into a spreadsheet, or refresh it on a schedule.

Python example:

from apify_client import ApifyClient

client = ApifyClient("<YOUR_APIFY_TOKEN>")
run = client.actor("rel8ble/xing-jobs-scraper").call(run_input={
    "searchQueries": ["SAP Berater"],
    "locations": ["München", "Frankfurt am Main"],
    "postedWithinDays": 7,
    "maxItems": 100,
})
for job in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(job["title"], "|", job["companyName"], "|", job["city"], "|", job["salaryMin"], job["salaryCurrency"], "|", job["url"])

Limits (read before large runs)

• 1,000 jobs per single search is XING's own limit, and its paging repeats some ads on big searches. Deep search splits any search above 500 jobs into narrower ones; a slice that is still above 1,000 after every split is paged to the limit and counted in the run summary (searchesCappedAt1000). On the largest test (every job in Hamburg) 99% of the listed jobs came back.
• XING searches by meaning. Controller also returns related finance and consulting roles (about 4 in 10 results in testing). Turn on strictKeywordMatch to keep only ads whose title or XING keywords contain your keyword; the skipped ads are not charged. For keyword searches XING only reports "1,000+" as the total, so the run summary shows siteTotalIsLowerBound.
• Cities, not countries. XING's public search has no country filter. countries is applied to each job after the search, so "all of Switzerland" pages through jobs from everywhere (fine for small numbers; for big pulls, list a few Swiss cities instead). A city XING does not recognise returns 0 jobs and the run summary says so (XING itself would silently search everything).
• Remote filter and cities. With workplaceTypes: ["remote"], XING also returns fully remote jobs from other cities for a city search, because they can be done from there.
• Salaries are mostly estimates. About a fifth of ads state a salary; XING estimates the rest. salarySource tells them apart. Figures are yearly gross amounts.
• No sort order. XING's API ignores the sort parameter (tested with a dozen values, including date and relevance), so results come in XING's relevance order. Use postedWithinDays to get the newest jobs.
• No personal data. Contact-person blocks are not requested, e-mail apply addresses and recruiter profiles are never returned, and e-mail addresses and phone numbers written inside the ad text are replaced with [email removed] / [phone removed]. Names written inside the free text of an ad can still appear there.

FAQ

Is there an official XING API for job search? XING offers no public API for reading its job ads. This actor is an unofficial alternative that reads the public job search, without a login.

Do I need a XING account or cookies? No. Everything comes from the public, logged-out job search.

How do I get only new jobs every day? Schedule the actor daily with postedWithinDays: 1 and deduplicate on id in your own system. XING counts refreshed ads as new, so some older ads come back when they are republished; postedAt shows the first publication date.

Can I get English labels for German ads? Yes: language: "en" (default) returns the employment type, career level, job field, industry and country in English. The ad text stays in the language the employer wrote it in; adLanguage tells you which.

Why did I get fewer jobs than XING's count? XING's count includes ads that appear twice across pages and moves while a big run is going; duplicates are dropped and not charged. Check RUN_SUMMARY in the key-value store: it lists each search with the site total, pages fetched and why it stopped.

Is scraping XING legal? The actor collects publicly visible job ads, not personal data. You are responsible for how you use the data and for following XING's terms and the laws that apply to you (including the GDPR).

Something is wrong or a field is missing. Open an issue on the Issues tab of the actor page with the run ID. It reaches the developer directly.