Arbeitnow Jobs Scraper - German Jobs, EU Remote & On-site
Pricing
from $2.00 / 1,000 job returneds
Arbeitnow Jobs Scraper - German Jobs, EU Remote & On-site
Scrape the Arbeitnow job board - Germany and Europe weighted, remote and on-site, around 1,075 listings - into a clean JSON or CSV dataset. Normalised hiring regions, employment type, seniority, salary where published, and a ghost-job score flagging expired and stale job postings.
Pricing
from $2.00 / 1,000 job returneds
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Dave Fergins
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Arbeitnow Jobs Scraper
Scrape the Arbeitnow job board - Germany and Europe weighted, remote and on-site, around 1,075 listings - into a clean JSON or CSV dataset. Normalised hiring regions, employment type, seniority, salary where published, and a ghost-job score flagging expired and stale job postings.
Arbeitnow jobs: Germany and EU, remote and on-site
Arbeitnow is the largest single feed of the six job boards this pipeline supports β around 1,075 listings β and the only one weighted towards Germany and continental Europe rather than the US.
It is also the only one carrying on-site roles alongside remote ones, and it publishes an honest remote flag saying which is which. That flag is respected rather than assumed, so remoteOnly genuinely means something here:
remoteOnly: true(default) β remote roles onlyremoteOnly: falseβ the full board, on-site included
German employment vocabulary is normalised too: Vollzeit and Teilzeit land on the same full_time / part_time values as every English board, so a filter written once works across all of them.
The board's own API asks users not to abuse it, so requests are paced and page-budgeted.
Arbeitnow job data: what each row contains
| Identity | stable id across runs, posting URL, direct applyUrl where published |
| The role | title, company, plain-text description, tags, employment type, seniority |
| Where | remote flag, the board's own location text, and normalised regions (worldwide / usa / canada / latam / uk / europe / apac / africa / middle_east) |
| Pay | min, max, currency and period as numbers, annualised on request |
| When | posted date, expiry where published, age in days |
| Trust | freshness 0-1, ghostRisk low/medium/high, and ghostReason explaining the verdict in plain words |
Ghost job detection: the trust fields
Job boards are full of postings still published but no longer open β filled roles left up for pipeline, evergreen "talent pool" adverts, and listings nothing ever expires. Every row here carries a risk band and the reasons behind it:
{"title": "Senior Backend Engineer","company": "Acme","freshness": 0.71,"ageDays": 10.4,"ghostRisk": "low","ghostReason": ["recent, and nothing contradicts it"]}
Signals come from what the board actually publishes: age, its own expiry date where there is one, missing application links, and evergreen phrasing. Nothing is inferred by a model. Freshness decays on a 21-day half-life, and a posting with no date scores 0.5 rather than 1.0 β absence of evidence is not evidence of freshness.
Example input: full-time Java or Kotlin jobs in Europe, on-site included
{"query": ["java", "kotlin"],"regions": ["europe"],"employmentTypes": ["full_time"],"remoteOnly": false,"maxAgeDays": 30,"maxGhostRisk": "medium","maxItems": 300}
Every field is optional β run it empty and you get the remote roles on the Arbeitnow board (remoteOnly is on by default; turn it off for the full board of around 1,075 listings), best-first.
- Search terms are OR-ed.
["go", "rust"]returns jobs mentioning either. - Worldwide jobs match every region filter, because a job open to everyone is open to you.
minSalaryis annualised first, so hourly and monthly rates compare correctly.maxItemsis your cost ceiling β you are billed per job returned.maxPagesPerSource: Arbeitnow paginates at 175 jobs per page. 10 pages (the default) reaches the end of the feed, which is around 1,075 jobs.
Output: one dataset item per Arbeitnow job
One dataset item per job, ordered best-first: lowest ghost-job risk, then freshest. Export as JSON, CSV or Excel, or read it from the API like any Apify dataset.
If Arbeitnow itself returns no jobs β the board is down or has changed its format β the run fails with a clear status message instead of succeeding with an empty dataset, so a failed-run alert on a schedule catches it. A run whose filters match nothing still succeeds, with an empty dataset and a status line saying so. When fewer than half of the rows carry a company or a posted date, the run log prints a WARNING.
Arbeitnow jobs scraper pricing: a worked cost example
You pay per job returned, on Apify's pay-per-event model, and nothing for platform usage on top: $0.006 per job on the Free and Bronze plans, $0.003 on Silver, $0.002 on Gold, Platinum and Diamond. Rows dropped by your filters are never billed, and a run that returns nothing costs nothing.
| Run | Jobs returned | Free / Bronze | Silver | Gold and above |
|---|---|---|---|---|
The full board, remoteOnly: false, 10 pages | 1,075 | $6.45 | $3.23 | $2.15 |
A run capped with maxItems: 300 (at most) | 300 | $1.80 | $0.90 | $0.60 |
| A run whose filters match nothing | 0 | $0.00 | $0.00 | $0.00 |
maxItems is the hard ceiling: maxItems: 200 can never cost more than $1.20 on the Free plan. Apify's free plan includes a monthly usage credit, so a first run costs nothing out of pocket.
Use cases for Arbeitnow job data
- European and German hiring data. Arbeitnow is Europe-weighted and Germany-heavy, which makes it the source here for EU job market data rather than US-centric remote listings.
- On-site and hybrid roles, not just remote. This is the only board in this pipeline that carries them, so it is the one to use when you need the whole market and not the work-from-home slice.
- Salary benchmarking and compensation data. Where this board publishes pay,
salaryMin,salaryMax,salaryCurrencyandsalaryPeriodcome through as numbers, andminSalaryannualises before comparing. - Filtering out expired and stale job postings.
ghostRisk,ghostReasonandfreshnesssay which listings are probably no longer open, so you can drop them before anyone wastes an application. - Job market and hiring data. Run it on a schedule and track how hiring, salary ranges, seniority mix and regions move over time.
- Building a job board, job feed or job alerts.
idis stable across runs, so diffing today's dataset against yesterday's gives you genuinely new jobs rather than a board reshuffle. - Recruitment and talent research. Company, title, tags, seniority, employment type and normalised regions on every row.
Export as JSON, CSV or Excel, or read the dataset straight from the Apify API.
How this Arbeitnow scraper fetches jobs
Arbeitnow publishes a public, documented, unauthenticated JSON feed and this Actor reads it β paced, budgeted, with an honest user agent. No HTML scraping, no bot-check evasion, no personal data. Job adverts only.
How to scrape Arbeitnow jobs with this Actor
- Open the Actor and press Start. With no input it returns the remote roles on the Arbeitnow board (
remoteOnlyis on by default; turn it off for the full board of around 1,075 listings), best-first. - Narrow it when you need to: search terms, hiring regions, seniority, a minimum salary, a maximum ghost-job risk, and
maxItemsas a hard cost ceiling. - Export the dataset as JSON, CSV or Excel, read it through the Apify API, or schedule the run and attach a webhook or an integration (Google Sheets, Make, Zapier, Slack) so new Arbeitnow jobs arrive on their own.
Related Actors from the same pipeline
- Remote Jobs Aggregator β six boards in one call, deduplicated, with salary parsing and ghost-job scoring
- Himalayas Remote Jobs Scraper β the board with real expiry dates and numeric timezone restrictions
- RemoteOK Remote Jobs Scraper β genuinely remote developer jobs, the on-site leakage filtered out
- Remotive Jobs Scraper β a small, hand-curated board of remote tech jobs
- We Work Remotely Jobs Scraper β company split from title, real expiry dates
- Jobicy Remote Jobs Scraper β the cleanest structured salary data for benchmarking
- Dice Jobs Scraper β US tech jobs with salary parsed to the cent
All eight share one schema, one ghost-job model and one set of filters, so a query written for this board runs unchanged against any of the others.
FAQ: scraping Arbeitnow jobs
Does Arbeitnow have an official API, and is it legal to scrape Arbeitnow jobs? Arbeitnow publishes a public, documented, unauthenticated JSON feed and this Actor reads it β paced, budgeted, with an honest user agent. No HTML scraping, no bot-check evasion, no personal data. Job adverts only.
How many Arbeitnow jobs can one run return?
Up to the whole board. Arbeitnow serves 175 jobs per page and the default 10 pages reaches the end of the feed, around 1,075 listings with remoteOnly off; the default remote-only run returns the remote subset of that.
How often should I run this Arbeitnow scraper, and can I get only the new jobs?
A daily schedule keeps the dataset current. id is stable across runs, so diffing today's dataset against yesterday's yields only the genuinely new Arbeitnow jobs, not a reshuffle; maxAgeDays set to 1 or 2 is the cheaper version of the same idea.
How do I get only fresh, still-open Arbeitnow jobs?
Set maxGhostRisk to low and maxAgeDays to 14 or 21. Every row also carries ghostReason, the plain-language grounds for its rating, so you can apply your own rule instead.
What is a ghost job?
A posting that is still published but no longer open: a filled role left up for pipeline, an evergreen "talent pool" advert, or a listing on a board that never expires anything. The ghostRisk band and freshness score flag them from what the board itself publishes β age, expiry dates, missing application links, evergreen phrasing β never from a model's guess.
Does it include salary data?
Only where Arbeitnow publishes it, which is a minority of listings; those rows carry salaryMin, salaryMax, salaryCurrency and salaryPeriod as numbers. Set salaryOnly to keep only those.
Do I need proxies, a login or an API key? No. Arbeitnow is read the way it asks to be read, and Apify runs the Actor for you; nothing to install and no credentials to manage.
Can I export Arbeitnow jobs to CSV, Excel or Google Sheets? Yes. Every run's dataset downloads as JSON, CSV or Excel from the Apify Console or API, and the Google Sheets integration writes rows straight into a sheet.
Is this a remote job scraper I can run as an API? Yes. Apify runs every Actor from its API as well as the Console: start a run over HTTP with the same JSON input shown above, or put it on a schedule, then read the finished dataset as JSON, CSV or Excel. The dataset is the output; there is no separate query endpoint, so a scheduled run plus a dataset read is the API.
Can an AI agent use this Arbeitnow scraper? Yes, through Apify's own MCP server, which exposes Store Actors as tools to Claude and other MCP clients. Nothing here is agent-specific: the agent sends the same JSON input and reads the same dataset.
How do I drop expired job listings?
Set maxGhostRisk to low or medium and they are dropped before they are returned, so you do not pay for them. Every row that is returned carries ghostRisk (low, medium or high), ghostReason (the plain-language grounds, such as "board's own expiry date passed 3 days ago" or "posted 64 days ago") and freshness (0-1, decaying on a 21-day half-life, 0 once a published expiry date has passed), so you can apply your own rule after export instead. maxAgeDays drops anything older than N days, including rows with no posted date.
What does a Arbeitnow job row not contain?
No visa-sponsorship flag, no applicant counts, no recruiter emails or any personal data, and no company size β Arbeitnow does not publish them in its feed, so they are not invented here. A blank seniority means the board never said; a blank salary means unpublished, not zero.
Want more than one board?
Remote Jobs Aggregator runs this same pipeline across six boards at once β Remote OK, Remotive, Himalayas, Arbeitnow, We Work Remotely and Jobicy β folding duplicates and recording which boards carry each job.
Measured while building it: those six boards barely overlap. Only 1 of 1,513 company+title pairs appeared on more than one. The aggregator is not about removing duplication β there is almost none β it is about getting six boards' worth of distinct jobs from one call.