Jobnet.dk Scraper - Denmark Public Job Portal

What does Jobnet Scraper do?

Jobnet Scraper extracts structured job data from jobnet.dk — including contact details (email, apply URL), company metadata, full descriptions, and location data. It supports keyword search and controllable result limits, so you can run the same query consistently over time. The actor also offers detail enrichment (full descriptions, company metadata, and contact information) where the source provides them.

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Key features

• ♻️ Incremental mode — recurring runs emit only NEW / UPDATED / REAPPEARED records — UNCHANGED and EXPIRED are opt-in. First run builds the baseline; subsequent runs emit and charge only for the diff. Pair with notifications for daily "new jobs" alerts to your hiring team. Saves 80–95% on daily monitoring.
• 🔔 Notifications — Telegram, Slack, Discord, WhatsApp Cloud API, generic webhook — out of the box. Pair with incremental + notifyOnlyChanges for daily "new Jobnet jobs" pings to your hiring channel.
• 📋 Detail enrichment — two-stage mode: list, then enrich each job with the full description + detail-page fields (apply counts, education, etc.). One toggle, no extra orchestration.
• 📧 Email + phone extraction — every record carries extractedEmails[] and extractedPhones[] regex-pulled from the description — direct-outreach lists with no extra processing step.
• 🔗 URL + social-profile extraction — every record carries extractedUrls[] and structured socialProfiles { linkedin, twitter, github, … } parsed from the description — useful when employers drop their careers page or recruiter LinkedIn in-line.
• 📦 Compact mode — AI-agent and MCP-friendly compact payloads with core fields only — pipe straight into your ATS, salary-benchmarking tool, or LLM context without parsing extras.
• 📝 Description format selection — pick a single description representation — text, html, or markdown — and the unused variants are dropped from each record. Halves payload size when your pipeline only consumes one format.
• 🧹 Empty-field stripping — drop null, empty-string, and empty-array fields from each record before push. Smaller payloads for AI agents and dashboards that already handle missing fields gracefully.
• 📌 Change classification — each record carries a changeType of NEW / UPDATED / UNCHANGED / REAPPEARED / EXPIRED. Default emits NEW + UPDATED + REAPPEARED; opt into the others with emitUnchanged / emitExpired. Repost detection flags previously-expired listings that come back.
• 📤 Export anywhere — Download the dataset as JSON, CSV, or Excel from the Apify Console, or stream live via the Apify API and integrations (Make, Zapier, Google Sheets, n8n, …).

What data can you extract from jobnet.dk?

Each result includes Core listing fields (jobId, title, cvrNumber, occupation, municipality, postalCode, postalDistrictName, and country, and more), detail fields when enrichment is enabled (description), contact and apply information (contactPersons and applyUrl), and company metadata (employerName and employerCity). In standard mode, all fields are always present — unavailable data points are returned as null, never omitted. In compact mode, only core fields are returned.

Enable detail enrichment in the input to get richer fields such as full descriptions, company metadata, and contact information where the source provides them.

Input

The main inputs are a search keyword and a result limit. Additional filters and options are available in the input schema.

Key parameters:

• query — Job search keywords. Leave empty to browse all listings.
• region — Filter by Danish region. (default: "")
• employmentType — Permanent or temporary position. (default: "")
• workHoursType — Full time, part time, or non-fixed hours. (default: "")
• kmRadius — Geographic search radius in km. Applies when a postal code is used. (default: 50)
• maxResults — Maximum total results to return (0 = unlimited). (default: 25)
• includeDetails — Fetch full job description and contact data from detail endpoint. (default: true)
• descriptionMaxLength — Truncate description to N characters. 0 = no truncation. (default: 0)
• compact — Return core fields only — useful for AI-agent and MCP workflows. (default: false)
• descriptionFormat — Pick a single description representation. all keeps every variant; text / html / markdown drop the others. (default: "all")
• excludeEmptyFields — Drop null, empty-string, and empty-array fields from each record before push. Smaller payloads for AI agents and dashboards. (default: false)
• incrementalMode — Only return new or changed listings since last run. (default: false)
• ...and 13 more parameters

Input examples

Basic search — Keyword-driven search with a result cap.

→ Full payload per result — all standard fields populated where the source provides them.

{
  "query": "softwareudvikler",
  "maxResults": 50
}

Incremental tracking — Only emit jobs that changed since the previous run with this stateKey.

→ First run builds the baseline state. Subsequent runs emit only records that are new or whose tracked content changed. Set emitUnchanged: true to include unchanged records as well.

{
  "query": "softwareudvikler",
  "maxResults": 200,
  "incrementalMode": true,
  "stateKey": "softwareudvikler-tracker"
}

Compact filtered output — Combine filters with compact mode for a lightweight AI-agent or MCP data source.

→ Core fields only — ideal for piping into LLMs or downstream tools without token overhead.

{
  "query": "softwareudvikler",
  "employmentType": "",
  "maxResults": 50,
  "compact": true
}

Output

Each run produces a dataset of structured job records. Results can be downloaded as JSON, CSV, or Excel from the Dataset tab in Apify Console.

Example job record

{
  "jobId": "54ac467e-38d2-454a-b7ad-ada6ece246ad",
  "title": "Børnehuset Lille Tornhøj i Aalborg Øst søger 3 pædagoger",
  "employerName": "www.aalborg.dk",
  "cvrNumber": "29189420",
  "occupation": "Pædagog",
  "municipality": "Aalborg",
  "postalCode": 9220,
  "postalDistrictName": "Aalborg Øst",
  "country": "Danmark",
  "isPartTime": false,
  "isExternal": false,
  "noFixedWorkplace": false,
  "isDisabilityFriendly": false,
  "datePosted": "2026-04-04T00:00:00+02:00",
  "applicationDeadline": "2026-04-16T10:00:00+02:00",
  "applicationDeadlineStatus": "ExpirationDate",
  "availablePositions": 1,
  "description": "<p>Vi søger dig, som brænder for at give børn en udviklende og legende dag. Dig, som skaber tryghed og nærvær. Dig, som har øje for fællesskabet og kan bevare overblikket. Dig, som er bevidst om egen...",
  "employerCity": null,
  "contactPersons": [
    {
      "name": null,
      "email": null,
      "phone": "25200560",
      "title": null
    }
  ],
  "applyUrl": "https://aalborg.career.emply.com/ad/bornehuset-lille-tornhoj-i-aalborg-ost-soger-3-paedagoger/trzkoa/da",
  "externalUrl": null,
  "portalUrl": "https://jobnet.dk/find-job/54ac467e-38d2-454a-b7ad-ada6ece246ad",
  "viewCount": 15,
  "scrapedAt": "2026-04-04T17:27:26.833Z",
  "source": "jobnet.dk",
  "changeType": null
}

Incremental fields

When incremental: true, each record also carries:

• changeType — one of NEW, UPDATED, UNCHANGED, REAPPEARED, EXPIRED.
• firstSeenAt, lastSeenAt — ISO-8601 timestamps tracking the listing across runs.
• isRepost, repostOfId, repostDetectedAt — populated when a new listing matches the tracked content of a previously expired one. Set skipReposts: true to drop detected reposts from the output.

How to scrape jobnet.dk

1. Go to Jobnet Scraper in Apify Console.
2. Enter a search keyword.
3. Set maxResults to control how many results you need.
4. Enable includeDetails if you need full descriptions, contact info, company data.
5. Click Start and wait for the run to finish.
6. Export the dataset as JSON, CSV, or Excel.

Use cases

• Extract job data from jobnet.dk for market research and competitive analysis.
• Monitor new and changed listings on scheduled runs without processing the full dataset every time.
• Build outreach lists using contact details and apply URLs from listings.
• Research company hiring patterns, employer profiles, and industry distribution.
• Use structured location data for regional analysis, mapping, and geo-targeting.
• Feed structured data into AI agents, MCP tools, and automated pipelines using compact mode.
• Export clean, structured data to dashboards, spreadsheets, or data warehouses.

How much does it cost to scrape jobnet.dk?

Jobnet Scraper uses pay-per-event pricing. You pay a small fee when the run starts and then for each result that is actually produced.

• Run start: $0.01 per run
• Per result: $0.003 per job record

Example costs:

• 10 results: $0.04
• 100 results: $0.31
• 500 results: $1.51

Example: recurring monitoring savings

These examples compare full re-scrapes with incremental runs at different churn rates. Churn is the share of listings that are new or whose tracked content changed since the previous run. Actual churn depends on your query breadth, source activity, and polling frequency — the scenarios below are examples, not predictions.

Example setup: 100 results per run, daily polling (30 runs/month). Event-pricing examples scale linearly with result count.

Churn rate	Full re-scrape run cost	Incremental run cost	Savings vs full re-scrape	Monthly cost after baseline
5% — stable niche query	$0.31	$0.03	$0.28 (92%)	$0.75
15% — moderate broad query	$0.31	$0.06	$0.26 (82%)	$1.65
30% — high-volume aggregator	$0.31	$0.10	$0.21 (68%)	$3.00

Full re-scrape monthly cost at daily polling: $9.30. First month with incremental costs $1.04 / $1.91 / $3.21 for the 5% / 15% / 30% scenarios because the first run builds baseline state at full cost before incremental savings apply.

FAQ

How many results can I get from jobnet.dk?

The number of results depends on the search query and available listings on jobnet.dk. Use the maxResults parameter to control how many results are returned per run.

Does Jobnet Scraper support recurring monitoring?

Yes. Enable incremental mode to only receive new or changed listings on subsequent runs. This is ideal for scheduled monitoring where you want to track changes over time without re-processing the full dataset.

Can I integrate Jobnet Scraper with other apps?

Yes. Jobnet Scraper works with Apify's integrations to connect with tools like Zapier, Make, Google Sheets, Slack, and more. You can also use webhooks to trigger actions when a run completes.

Can I use Jobnet Scraper with the Apify API?

Yes. You can start runs, manage inputs, and retrieve results programmatically through the Apify API. Client libraries are available for JavaScript, Python, and other languages.

Can I use Jobnet Scraper through an MCP Server?

Yes. Apify provides an MCP Server that lets AI assistants and agents call this actor directly. Use compact mode and descriptionMaxLength to keep payloads manageable for LLM context windows.

Is it legal to scrape jobnet.dk?

This actor extracts publicly available data from jobnet.dk. Web scraping of public information is generally considered legal, but you should always review the target site's terms of service and ensure your use case complies with applicable laws and regulations, including GDPR where relevant.

Your feedback

If you have questions, need a feature, or found a bug, please open an issue on the actor's page in Apify Console. Your feedback helps us improve.

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Getting started with Apify

New to Apify? Create a free account with $5 credit — no credit card required.

1. Sign up — $5 platform credit included
2. Open this actor and configure your input
3. Click Start — export results as JSON, CSV, or Excel

Need more later? See Apify pricing.