Get every new Xing job, with salaries, into a spreadsheet every morning

If you work with the German, Austrian or Swiss job market, whether in recruiting, HR tech,
labour-market research or a job board, you have probably wanted Xing's job ads as data instead of
web pages. Xing is the leading professional network in the DACH region, with around half a million
active job postings at any time and thousands of new ones every day.

In this post I show how to get them as clean JSON, CSV or Excel. That includes salary ranges,
remote/hybrid information and career level, and a daily feed that only returns the jobs published
since yesterday. You don't need to write a scraper: we'll use a ready-made one on Apify, called
from Python.

What you get for each job

{ "title": "Python Developer Financial Services (all genders)", "company": "Exxeta", "companySize": "1.001-5.000 Mitarbeitende", "city": "Berlin", "workplace": "hybrid", "employmentType": "FULL_TIME", "careerLevel": "Mit Berufserfahrung", "discipline": "IT und Softwareentwicklung", "language": "de", "salaryMin": 59000, "salaryMax": 86000, "salaryCurrency": "EUR", "salaryIsEstimate": true, "datePosted": "2026-09-17T05:36:36Z", "applyUrl": "https://career.exxeta.com/...", "description": "..." }

Salaries are the interesting part. Many employers don't publish one, so Xing shows its own estimate
instead. The data keeps them apart with salaryIsEstimate, so you can choose whether to trust
estimates.

1. Get a token

Create a free Apify account and copy your API token from
Settings → API & Integrations. The free plan includes monthly credits, and the scraper costs
$1 per 1,000 jobs, so testing is basically free.

pip install apify-client export APIFY_TOKEN="your-token"

2. Search by keyword and city

import os from decimal import Decimal from apify_client import ApifyClient client = ApifyClient(os.environ["APIFY_TOKEN"]) run = client.actor("angelrxdriguez/xing-jobs-scraper").call( run_input={"mode": "search", "keywords": ["python"], "locations": ["Berlin"], "maxItems": 50}, max_total_charge_usd=Decimal("0.10"), # hard cap for this run ) dataset_id = run.default_dataset_id # run["defaultDatasetId"] on apify-client < 3  for job in client.dataset(dataset_id).iterate_items(): print(job["title"], "|", job["company"], "|", job.get("workplace"), "|", job.get("salaryMax"))

Every keyword is searched in every city you list, and a single search can return more than
1,000 jobs.

3. Filter before you pay

The filters run inside the scraper, so jobs that don't match are skipped and not charged. For
example, English-language remote or hybrid jobs that pay at least €60k:

run_input = { "mode": "search", "keywords": ["software engineer", "data engineer"], "locations": ["Berlin", "München", "Hamburg"], "languages": ["en"], "workplace": ["remote", "hybrid"], "minSalary": 60000, }

If you're an English speaker looking for a job in Germany, that one query already saves you an
evening of scrolling.

4. A daily feed of only the new jobs

This is the mode I use the most. Instead of searching, feed mode reads Xing's public sitemaps,
which list every active job, and onlyNewJobs remembers what you already received. Schedule it
once a day and each run returns only the jobs published since the previous one:

run_input = { "mode": "feed", "feedCities": ["Berlin", "Hamburg", "München"], "feedKeywords": ["entwickler", "developer", "engineer"], "onlyNewJobs": True, "stateKey": "my-daily-feed", }

The examples repo has a complete script that
appends the new jobs to a CSV, ready for cron or Windows Task Scheduler. You can also skip code
entirely: create a schedule in the Apify Console and connect the results to Google Sheets, Make or
n8n.

A note on doing this responsibly

The scraper only uses pages that Xing's robots.txt allows for all crawlers. It collects job ads,
not people's profiles, and it removes e-mail addresses and phone numbers from descriptions by
default, so you don't end up storing recruiters' personal data. If you're in the EU, that matters
for GDPR.