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GermanTechJobs.de Scraper

Scrape tech job listings from germantechjobs.de, Germany’s tech-only job board and part of the DevITjobs network.

Unlike listing-only GermanTechJobs scrapers, this actor fetches every job’s detail page. This provides the full job description, mandatory and nice-to-have requirements, responsibilities, and engineering-practice signals such as Scrum, unit tests, code reviews, integration tests, and more.

What it collects

Each dataset record represents one job listing and can include:

• Job title, company, location, salary, workplace model, experience level, and technologies
• Full job description
• Mandatory requirements (requirementsMust)
• Nice-to-have requirements (requirementsNice)
• Responsibilities
• Engineering practices in engineeringPractices
• Visa sponsorship, company, partner, category, and source metadata

How it works

The actor runs in two stages:

1. Listing discovery
   It renders GermanTechJobs listing pages in a stealth headless browser using Camoufox through an Apify Residential proxy. It collects job detail-page URLs.

2. Detail-page extraction
   It fetches each job detail page over fast HTTP using curl_cffi with Chrome impersonation and a fresh residential proxy session per request. The actor parses the embedded job object from the page.

Use direct detail URLs in startUrls when you already know the jobs to scrape. Direct URLs skip listing discovery.

Input

Field	Type	Description
keyword	string	Filter jobs by a keyword. Applied client-side to parsed job records.
city	string	Filter by city. Applied client-side.
techCategory	string	Filter by technology or role category. Applied client-side.
technologies	array of strings	Filter by technologies. Matches jobs containing any selected technology. Applied client-side.
minSalary	number	Minimum annual salary in EUR. Compared with annualSalaryFrom.
visaSponsorshipOnly	boolean	Return only jobs that advertise visa sponsorship.
daysOld	number	Return jobs published within the requested number of days.
maxResults	number	Maximum number of jobs to deliver. Default: 50.
startUrls	array of strings	Direct GermanTechJobs job detail URLs. These skip listing discovery.
fetchDetails	boolean	Fetch and parse job detail pages, including descriptions, requirements, responsibilities, and engineering practices.
proxyConfiguration	object	Proxy configuration. Apify RESIDENTIAL proxy is required and enabled by default.

All filters are applied client-side after records are parsed.

Example input

{
  "keyword": "Python",
  "city": "Berlin",
  "technologies": ["Python", "Django", "AWS"],
  "minSalary": 65000,
  "visaSponsorshipOnly": true,
  "daysOld": 14,
  "maxResults": 25,
  "fetchDetails": true,
  "proxyConfiguration": {
    "useApifyProxy": true,
    "apifyProxyGroups": ["RESIDENTIAL"]
  }
}

Output

The actor saves one dataset record per job.

Field	Description
id	GermanTechJobs job identifier.
title	Job title.
company	Hiring company name.
url	Job detail-page URL.
slug	Job URL slug.
city	Job city.
cityCategory	Location category shown by the source.
postalCode	Postal code, when available.
address	Job address, when available.
latitude	Location latitude, when available.
longitude	Location longitude, when available.
workplace	Workplace model, such as remote, hybrid, or on-site.
jobType	Employment type.
expLevel	Experience level.
language	Job-posting language.
techCategory	Technology or role category.
metaCategory	Higher-level source category.
technologies	Technologies associated with the job.
filterTags	Source filter tags associated with the listing.
annualSalaryFrom	Minimum advertised annual salary.
annualSalaryTo	Maximum advertised annual salary.
hasSalary	Whether salary information is available.
salaryCurrency	Salary currency, typically EUR.
companySize	Company-size category, when available.
companyType	Company-type category, when available.
hasVisaSponsorship	Whether the job advertises visa sponsorship.
candidateContactWay	Candidate contact method, when available.
sourcePartner	Source partner information, when available.
isPartner	Whether the listing is from a partner source.
tier	Source listing tier, when available.
description	Full job description from the detail page.
requirementsMust	Mandatory requirements from the detail page.
requirementsNice	Nice-to-have requirements from the detail page.
responsibilities	Responsibilities from the detail page.
engineeringPractices	Engineering-practices dictionary, such as scrum, unitTests, codeReviews, and integrationTests.
publishDate	Job publication date, when available.
source	Source website.
scrapedAt	Timestamp when the record was scraped.

Example output record

{
  "id": "123456",
  "title": "Senior Python Developer",
  "company": "Example GmbH",
  "url": "https://germantechjobs.de/en/jobs/Example-GmbH-Senior-Python-Developer-Berlin",
  "slug": "Example-GmbH-Senior-Python-Developer-Berlin",
  "city": "Berlin",
  "workplace": "Hybrid",
  "jobType": "Full-time",
  "expLevel": "Senior",
  "technologies": ["Python", "Django", "PostgreSQL", "AWS"],
  "annualSalaryFrom": 70000,
  "annualSalaryTo": 90000,
  "hasSalary": true,
  "salaryCurrency": "EUR",
  "hasVisaSponsorship": true,
  "description": "We are looking for a Senior Python Developer to join our engineering team.",
  "requirementsMust": ["Professional Python experience", "Django knowledge", "SQL experience"],
  "requirementsNice": ["AWS experience", "Kubernetes knowledge"],
  "responsibilities": ["Build backend services", "Review code", "Collaborate with product teams"],
  "engineeringPractices": {
    "scrum": true,
    "unitTests": true,
    "codeReviews": true,
    "integrationTests": true
  },
  "publishDate": "2025-03-01",
  "source": "germantechjobs.de",
  "scrapedAt": "2025-03-08T10:30:00.000Z"
}

Pricing

This actor uses pay-per-event pricing.

One job-record event is charged for every job delivered to the dataset.

Usage statistics

This Actor creates a small, content-free summary at the end of each run. It is used only to monitor reliability and improve this Actor. A copy is saved as USAGE_STATS in your own Apify key-value store, so you can see the exact record created for your run.

Set disableUsageStats to true in the input to opt out. Nothing is sent then; your USAGE_STATS record only says that statistics were disabled.

Only these fields are recorded:

• schema version, Actor name and build number;
• UTC start and finish hour (not a precise timestamp);
• run duration, number of results and time to the first result, each as a coarse range;
• whether the result was empty, the end status, and an error type from a fixed list;
• memory setting and counts of charged events;
• names of the input fields you set, never their values;
• the selected option for input fields that offer a fixed list of choices (for example a sort order).

We do not collect input text, search terms, URLs, domains, usernames, email addresses, names, proxy credentials, tokens, scraped records, output items, raw error messages, stack traces, or hashes of any of those values. Records are kept for no longer than 13 months, used only as aggregated operational statistics, and never sold or shared.

Additional fields (Phase 2)

This Actor also records your Apify user ID, whether Apify marks the account as paying, the size range of list inputs, the selected country when the input offers a fixed list of countries, and one category from a fixed Actor taxonomy. We use these fields only for aggregate reliability, repeat-use and cross-Actor analysis; reports suppress any cell with fewer than five distinct users.

The same disableUsageStats: true input flag turns these fields off too. The user ID is removed after 13 months; we do not export, sell, share, or attempt to re-identify this data.

Run-outcome signals (v2)

To learn whether a run did what it was asked to do, the record also holds a few more coarse ranges and yes/no flags. None of them contains content:

• the result limit you asked for (a range, when the input has one) and what share of it was delivered;
• results delivered per input item you listed (a range);
• output quality as ranges: how fully the result fields were filled, the share of rows that look like errors, the share of duplicate rows, and how many different fields appeared. These are counted in memory while results are saved; no result content is kept;
• how the run was started (console, API, schedule, webhook, another Actor);
• how it ended: stopped by you, timed out, reached the requested limit, stopped by the charge limit, and how many times the platform moved the run;
• if this Actor reports it: how many items to process worked or failed (ranges) and one failure reason from a fixed list;
• a short code made from the names of the input fields you set, never their values.

Repeat-run fingerprint (v2)

When your Apify user ID is recorded (see above), the record also holds an 8-character one-way code made from your input (proxy settings left out) and this Actor's name. It only lets us see that the same account ran the same input again soon after an unsatisfying run; we never see the input itself. It is stored only in the database, never published, and reports use it in aggregate with the same five-user minimum. It is the one exception to the statement above that no hashes are collected, and disableUsageStats: true turns it off.