We extract user profiles, career histories, company metadata, and job listings from Xing. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for User Profiles objects from xing.com. All fields typed and schema-versioned.
"profile_id": "thomas_muller89", "first_name": "Thomas", "last_name": "Muller", "current_role": "Senior Backend Engineer", "location": "Munich, Bavaria, Germany", "premium_status": true, "profile_url": "https://www.xing.com/profile/thomas_muller89", "skills": "['Python', 'PostgreSQL', 'Docker', 'Kubernetes']"
| # | profile_id | first_name | last_name | current_role | location | languages |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Career History objects from xing.com. All fields typed and schema-versioned.
"profile_id": "thomas_muller89", "company_name": "TechLogix GmbH", "job_title": "Backend Engineer", "start_date": "2019-04", "end_date": "2023-08", "duration_months": 52, "location": "Berlin, Germany", "industry": "Software Development"
| # | profile_id | company_name | job_title | start_date | end_date | duration_months |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Company Pages objects from xing.com. All fields typed and schema-versioned.
"company_id": "techlogix-gmbh", "name": "TechLogix GmbH", "industry": "Information Technology", "employee_count": "501-1000", "hq_location": "Berlin, Germany", "website": "https://techlogix.de", "follower_count": 14205, "description": "Leading provider of enterprise cloud solutions in the DACH region."
| # | company_id | name | industry | employee_count | hq_location | website |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Job Listings objects from xing.com. All fields typed and schema-versioned.
"job_id": "job-984210", "title": "Data Engineer (m/w/d)", "company_name": "TechLogix GmbH", "location": "Remote / Munich", "work_type": "Full-time", "posted_date": "2023-10-15T08:30:00Z", "requirements": "['3+ years Python', 'Airflow', 'SQL']", "apply_url": "https://www.xing.com/jobs/984210"
| # | job_id | title | company_name | location | work_type | posted_date |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Educational Background objects from xing.com. All fields typed and schema-versioned.
"profile_id": "thomas_muller89", "institution": "Technical University of Munich", "degree": "Master of Science", "field_of_study": "Computer Science", "start_year": "2016", "end_year": "2018", "grade": "1.2", "activities": "Robotics Club, Open Source Contributor"
| # | profile_id | institution | degree | field_of_study | start_year | end_year |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Xing scraper handles the complexities of the DACH region's primary professional network: login walls, strict rate limits, and dynamic profile rendering.
Extract names, current roles, locations, skills, language proficiencies, and premium status indicators from public profiles.
Capture complete work histories including company names, job titles, tenures, and role descriptions.
Scrape company pages for industry classifications, employee count ranges, HQ locations, and follower metrics.
Track active job listings, required skills, benefits, and application URLs across targeted companies.
Extract university names, degree types, fields of study, and graduation years for talent mapping.
Geographically targeted extraction using German, Austrian, and Swiss residential proxy pools for accurate localized data.
Capture employer branding metrics and ratings linked directly from Xing company profiles.
Track job changes and profile updates over time with automated diffing and changelog generation.
Manage authenticated sessions and cookie rotation to access data hidden behind Xing's aggressive login prompts.
Brief in. Clean data out.
Provide Xing profile URLs, company IDs, or job search parameters. We map the extraction schema to your requirements.
We deploy Scrapy crawlers with DACH-region residential proxies, session management, and CAPTCHA solvers.
Schema validation, null-rate checks, and sample data review ensure field completeness before production launch.
Structured JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or via Webhook on schedule.
Xing heavily restricts automated access to protect its professional graph. Here is how we maintain reliable extraction.
Xing forces login prompts after minimal page views. We maintain pools of authenticated worker sessions, rotating cookies and user-agents to simulate natural browsing behaviour and avoid account bans.
Xing employs strict IP-based rate limiting. Our infrastructure distributes requests across thousands of residential IPs in Germany, Austria, and Switzerland, pacing requests to stay below detection thresholds.
Xing frequently updates its frontend architecture. We use Playwright for full JavaScript rendering and maintain fallback selector chains targeting stable data attributes rather than fragile CSS classes.
Xing profiles often mix German and English. Our pipeline standardises date formats, location hierarchies, and industry classifications into a clean, queryable schema regardless of the profile's display language.
Xing truncates search results for non-premium accounts. We bypass these limits by programmatically subdividing search queries by granular location and industry filters to extract the complete dataset.
HR teams build proprietary talent pools by tracking skill updates and job changes across the DACH region.
Sales teams extract decision-maker profiles, current titles, and company affiliations to enrich CRM records.
Companies monitor competitor hiring velocity, open roles, and employee turnover rates via company page data.
Aggregators and job boards ingest Xing job listings to provide comprehensive market coverage.
Consultancies analyze industry skill trends and geographic talent distribution across Germany, Austria, and Switzerland.
Universities track graduate career trajectories, current employers, and role progression for alumni engagement.
"Xing holds the definitive professional graph for the DACH region, but accessing this data at scale requires bypassing strict rate limits and complex login walls."
Most teams underestimate the engineering required to scrape Xing. Reliable extraction demands DACH-region residential proxies, session cookie management, and dynamic DOM parsing. DataFlirt handles the extraction infrastructure so your engineering team can focus on data modelling.
Everything supported by our xing.com scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy handles crawl orchestration and deduplication. Playwright executes JavaScript to render dynamic profile elements and handle complex login flows.
We maintain dedicated pools of residential ISP proxies in Germany, Austria, and Switzerland to ensure requests appear as local user traffic.
Pipelines run on Kubernetes clusters. Airflow handles scheduling and dependency management, ensuring reliable delivery against strict SLAs.
Data delivered to where your team already works — no new tooling required.
About xing.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available professional data is generally permissible, but must comply with GDPR regulations regarding PII. DataFlirt extracts only public profile data and does not bypass security controls to access private information. Clients must ensure their downstream use cases comply with regional data protection laws.
Xing requires authentication to view most profile data. We maintain pools of aged, authenticated worker accounts and rotate session cookies across requests to distribute load and prevent account restrictions.
Yes. Xing profiles often contain a mix of German and English text. Our pipeline extracts the raw text as entered by the user, and we can apply standardisation logic to normalise job titles and industries if required.
Throughput depends on the target volume and required stealth. For standard pipelines, we process approximately 10,000 to 50,000 profiles per day to maintain healthy proxy reputation and avoid triggering Xing's rate limits.
Yes. When scraping Xing company pages, we can follow links to Kununu to extract employer branding metrics, aggregate ratings, and review counts.
Yes. We offer a sample extraction of up to 500 profiles or 50 company pages to validate schema structure and data quality before formalizing an agreement.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off extraction of DACH region tech talent or continuous monitoring of competitor job listings, we build and operate the pipeline. Tell us your requirements.
