# Karriere Jobs Scraper (Cheap) (`data_api/karriere-jobs-scraper-cheap`) Actor Extract accurate, structured job listings from Austria’s premier career portal, Karriere.at. Collect titles, companies, salaries, locations, and more with support for multi-keyword searches, regional filters, automatic pagination, and enterprise-grade bypass infrastructure. - **URL**: https://apify.com/data\_api/karriere-jobs-scraper-cheap.md - **Developed by:** [Data API](https://apify.com/data_api) (community) - **Categories:** Jobs, Automation, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $3.99 / 1,000 results 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/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. 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. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/integrations/mcp.md). If your project is in a different language, use 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 ## Karriere Jobs Scraper Searching Karriere.at by hand means clicking through page after page and copying each role, company, and salary into a sheet by yourself. This scraper does the clicking for you. Give it a few job titles, optionally a city or two, and it walks the Karriere.at results and hands back every posting as a clean row with the title, employer, location, salary, and contract type already split into fields. It's fast, cheap, and code-free, and you only pay for what you scrape. ![Karriere Jobs Scraper](cover.jpg) ### What you get Each posting comes back as one row, grouped into three kinds of data: - **The role** — `positionTitle`, `listingUrl`, `contractType`, `payDetails`, `remoteOption`, `workLocations`, `postedDate` - **The employer** — `employerName`, `employerLogo`, `promotedListing`, `listingBadges` - **Run context** — `searchTerm`, `searchArea`, `collectedAt`, and `errorMessage` if a search could not be completed ### Quick start 1. Hit **Try for free** and type one or more job titles into **Search terms** (for example `projektmanager`). 2. Optionally add cities or regions under **Search areas** to narrow each search; leave it blank to cover all of Austria. 3. Set a **Results limit** to cap how many postings come back per search. 4. Press **Start**, then export the rows as JSON, CSV, Excel, or XML. ![How it works](how-it-works.jpg) ### Use cases - **Recruiters** — track which roles competitors are hiring for and where, then pitch candidates faster - **Job seekers** — pull every matching opening into one sheet instead of scrolling the site - **Salary research** — gather `payDetails` across a role or city to benchmark pay bands - **Market analysts** — measure hiring demand by employer, region, or contract type over time - **Recruitment tech** — feed live Karriere.at postings into your own job board or matching tool ### Input | Field | Type | Required | Description | |-------|------|----------|-------------| | `searchTerms` | array of strings | Yes | Role names or keywords to look up. Add several to run them in one pass. Prefilled with `projektmanager`. | | `searchAreas` | array of strings | No | Cities or regions to filter each search. Leave empty to search all of Austria. | | `resultsLimit` | integer | No | Most postings to collect per term and area pairing. Default `10`. | | `timeoutSeconds` | integer | No | Seconds to wait on each request before it times out. Default `45`. | #### Example input ```json { "searchTerms": ["projektmanager", "data analyst"], "searchAreas": ["Graz", "Salzburg"], "resultsLimit": 10, "timeoutSeconds": 45 } ```` ### Output Each search term runs against each area you supply, or once across Austria when no area is given, paginating automatically up to your limit. Every posting on the results becomes one row. Fields a card does not show come back as null rather than guessed. #### Example output ```json { "positionTitle": "Projektmanager (m/w/d)", "listingUrl": "https://www.karriere.at/jobs/8123456", "employerName": "Alpine Tech GmbH", "employerLogo": "https://www.karriere.at/assets/company/alpine-tech-logo.png", "promotedListing": false, "listingBadges": "Einblicke", "workLocations": "Graz", "contractType": "Vollzeit", "remoteOption": true, "payDetails": "ab € 3.500 brutto monatlich", "searchTerm": "projektmanager", "searchArea": "Graz", "postedDate": "vor 2 Tagen", "collectedAt": "2026-06-29T12:00:00.000000+00:00", "errorMessage": null } ``` #### Output fields | Field | Type | Description | |-------|------|-------------| | `positionTitle` | string | Headline of the advertised role | | `listingUrl` | string | Direct link to the posting on Karriere.at | | `employerName` | string | Company that posted the opening | | `employerLogo` | string | Link to the hiring company's logo | | `promotedListing` | boolean | True when the posting is sponsored or boosted | | `listingBadges` | string | Tags or labels on the card, such as Einblicke | | `workLocations` | string | Place or places where the job is based | | `contractType` | string | Working arrangement such as Vollzeit or Teilzeit | | `remoteOption` | boolean | True when home office or remote work is offered | | `payDetails` | string | Salary figure or range as printed on the card | | `searchTerm` | string | Keyword that surfaced this posting | | `searchArea` | string | City or region applied as a filter | | `postedDate` | string | When the listing went live, as shown on the card | | `collectedAt` | string | ISO 8601 timestamp of when the row was captured | | `errorMessage` | string | Reason a search failed; null on success | ### Tips for best results - **Be specific with search terms.** A tight keyword like `senior data analyst` returns more relevant rows than a broad one like `analyst`. - **Add areas only when you need them.** Each area multiplies the searches, so list a couple of cities rather than a long string of them. - **Start small with `resultsLimit`.** Run 10 first to confirm the output fits your pipeline, then raise it for the full pull. - **Some cards are sparse.** `payDetails` and `listingBadges` are blank when a posting omits them; the rest of the row still comes through. - **Raise `timeoutSeconds`** if you see timeouts while pulling large result sets. ### How can I use Karriere.at job data? **How can I use the Karriere Jobs Scraper to track hiring in Austria?** Add the roles you care about to `searchTerms`, optionally pin a few cities in `searchAreas`, and run it on a schedule. Each row carries `employerName`, `workLocations`, `contractType`, and `postedDate`, so you can watch who is hiring, where, and how that shifts week to week. **How can I collect Karriere.at salary data for benchmarking?** Pull a single role across several cities and read `payDetails` alongside `workLocations` and `contractType`. Export to CSV or Excel and you have a structured pay dataset to compare bands by region and arrangement. **How can I build a job dataset from Karriere.at for a job board?** Feed in the keywords your audience searches, set `resultsLimit` to the volume you need, and export. Every posting comes back with `positionTitle`, `listingUrl`, `employerName`, and `payDetails` ready to drop into your own board or matching engine. ### Is it legal to scrape data? Our actors are ethical and do not extract any private user data, such as email addresses or private contact information. They only extract what the user has chosen to share publicly. We therefore believe that our actors, when used for ethical purposes by Apify users, are safe. However, you should be aware that your results could contain personal data. Personal data is protected by the GDPR in the European Union and by other regulations around the world. You should not scrape personal data unless you have a legitimate reason to do so. If you're unsure whether your reason is legitimate, consult your lawyers. You can also read Apify's blog post on the [legality of web scraping](https://blog.apify.com/is-web-scraping-legal/). ### Support Questions, feature requests, or a field you'd like added? Reach out at and we'll get back to you. # Actor input Schema ## `searchTerms` (type: `array`): One or more role names or keywords to look up on Karriere.at. Add several to run them in a single pass, for example \["projektmanager", "data analyst"]. ## `searchAreas` (type: `array`): Optional list of cities or regions to narrow each search, such as \["Graz", "Salzburg"]. Leave it empty to search across all of Austria. ## `resultsLimit` (type: `integer`): Most postings to collect for each term and area pairing. Lower it for quick test runs, raise it for a full pull. ## `timeoutSeconds` (type: `integer`): How long to wait on each request before it gives up. Raise it if you run into frequent timeouts. ## Actor input object example ```json { "searchTerms": [ "project manager" ], "searchAreas": [], "resultsLimit": 10, "timeoutSeconds": 45 } ``` # 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 '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "searchTerms": [ "project manager" ], "searchAreas": [] }; // Run the Actor and wait for it to finish const run = await client.actor("data_api/karriere-jobs-scraper-cheap").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 '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "searchTerms": ["project manager"], "searchAreas": [], } # Run the Actor and wait for it to finish run = client.actor("data_api/karriere-jobs-scraper-cheap").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "searchTerms": [ "project manager" ], "searchAreas": [] }' | apify call data_api/karriere-jobs-scraper-cheap --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=data_api/karriere-jobs-scraper-cheap", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Karriere Jobs Scraper (Cheap)", "description": "Extract accurate, structured job listings from Austria’s premier career portal, Karriere.at. Collect titles, companies, salaries, locations, and more with support for multi-keyword searches, regional filters, automatic pagination, and enterprise-grade bypass infrastructure.", "version": "0.0", "x-build-id": "yGF4GfMqKsjiMctIn" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/data_api~karriere-jobs-scraper-cheap/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-data_api-karriere-jobs-scraper-cheap", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/data_api~karriere-jobs-scraper-cheap/runs": { "post": { "operationId": "runs-sync-data_api-karriere-jobs-scraper-cheap", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/data_api~karriere-jobs-scraper-cheap/run-sync": { "post": { "operationId": "run-sync-data_api-karriere-jobs-scraper-cheap", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "searchTerms" ], "properties": { "searchTerms": { "title": "Search terms", "type": "array", "description": "One or more role names or keywords to look up on Karriere.at. Add several to run them in a single pass, for example [\"projektmanager\", \"data analyst\"].", "items": { "type": "string" } }, "searchAreas": { "title": "Search areas", "type": "array", "description": "Optional list of cities or regions to narrow each search, such as [\"Graz\", \"Salzburg\"]. Leave it empty to search across all of Austria.", "items": { "type": "string" } }, "resultsLimit": { "title": "Results limit", "minimum": 1, "maximum": 1000, "type": "integer", "description": "Most postings to collect for each term and area pairing. Lower it for quick test runs, raise it for a full pull.", "default": 10 }, "timeoutSeconds": { "title": "Timeout (seconds)", "minimum": 5, "maximum": 120, "type": "integer", "description": "How long to wait on each request before it gives up. Raise it if you run into frequent timeouts.", "default": 45 } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```