# GermanTechJobs.de Scraper — Tech Jobs in Germany | PPE (`bovi/germantechjobs-de-scraper`) Actor

Scrape tech job listings from germantechjobs.de — Germany's tech-only job board. Returns title, company, city, salary range, technologies, visa sponsorship, plus the full description, requirements and responsibilities other scrapers leave empty. Pay per job delivered.

- **URL**: https://apify.com/bovi/germantechjobs-de-scraper.md
- **Developed by:** [Vitalii Bondarev](https://apify.com/bovi) (community)
- **Categories:** Jobs
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $1.07 / 1,000 germantechjobs.de scraper — tech jobs in germany | ppes

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For usage examples, see the [API](#api) section below.

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).

# README

null

## GermanTechJobs.de Scraper

Scrape tech job listings from [germantechjobs.de](https://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.

# Actor input Schema

## `keyword` (type: `string`):

case-insensitive filter over job title, technologies and description

## `city` (type: `string`):

filter by city or city region (e.g. Berlin, Munich)

## `techCategory` (type: `string`):

filter by tech category (e.g. Java, Python, DevOps)

## `technologies` (type: `array`):

keep jobs using ANY of these technologies

## `minSalary` (type: `integer`):

minimum annual salary in EUR (compared against annualSalaryFrom)

## `visaSponsorshipOnly` (type: `boolean`):

only jobs offering visa sponsorship

## `daysOld` (type: `integer`):

only jobs published within the last N days

## `maxResults` (type: `integer`):

maximum number of job records to return

## `startUrls` (type: `array`):

optional list of job DETAIL page URLs (https://germantechjobs.de/jobs/...) to scrape directly, skipping listing discovery

## `fetchDetails` (type: `boolean`):

fetch each job's detail page for full description, requirements and responsibilities (required for full data; kept for compatibility)

## `proxyConfiguration` (type: `object`):

proxy settings; Apify RESIDENTIAL proxy is required for reliable access

## Actor input object example

```json
{
  "visaSponsorshipOnly": false,
  "maxResults": 50,
  "fetchDetails": true,
  "proxyConfiguration": {
    "useApifyProxy": true,
    "apifyProxyGroups": [
      "RESIDENTIAL"
    ]
  }
}
```

# Actor output Schema

## `results` (type: `string`):

Dataset containing germantechjobs.de job records (id, title, company, city, technologies, salary, visa sponsorship, description).

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "proxyConfiguration": {
        "useApifyProxy": true,
        "apifyProxyGroups": [
            "RESIDENTIAL"
        ]
    }
};

// Run the Actor and wait for it to finish
const run = await client.actor("bovi/germantechjobs-de-scraper").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = { "proxyConfiguration": {
        "useApifyProxy": True,
        "apifyProxyGroups": ["RESIDENTIAL"],
    } }

# Run the Actor and wait for it to finish
run = client.actor("bovi/germantechjobs-de-scraper").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "proxyConfiguration": {
    "useApifyProxy": true,
    "apifyProxyGroups": [
      "RESIDENTIAL"
    ]
  }
}' |
apify call bovi/germantechjobs-de-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,bovi/germantechjobs-de-scraper"
        }
    }
}

```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/H0UltCrNfjgrslGAG/builds/okwhwAm3WqpDcWh9c/openapi.json