How to Scrape Arbeitnow Jobs (DACH & EU Remote) in 2026

Arbeitnow is one of the cleaner job boards to work with in the DACH region (Germany, Austria, Switzerland) — strong coverage of tech roles, remote positions, and crucially a meaningful slice of visa-sponsorship and relocation-friendly jobs. Unlike LinkedIn or Indeed, which wrap their listings in aggressive anti-bot layers, Arbeitnow publishes an honest public JSON feed. The work isn’t getting past a bot wall; it’s filtering and scheduling the feed into something useful. This guide covers how to do that for a continuously fresh DACH/EU-remote jobs feed in 2026.

What’s worth extracting

Each job record from Arbeitnow is a flat, clean object. The fields that matter:

• Identity — job title, company/employer name, a stable slug/ID.
• Location — city and country string (Berlin, München, Wien, Zürich, plus remote markers).
• Remote flag — a boolean indicating remote-eligible roles, the single most-filtered field for international candidates.
• Employment type — full-time, part-time, contract, internship.
• Tags / skills — an array of labels (e.g. react, kubernetes, english-speaking, visa), which double as both skill signals and the marker for relocation-friendly roles.
• Description — the full plain-text job description, cleaned of HTML.
• Apply link — the outbound URL to the application.
• Posting timestamp — when the job was published, normalized to ISO.

The tags array is the secret weapon here: Arbeitnow listings frequently tag visa, relocation and english-speaking, which makes filtering for international candidates trivial compared to other boards.

How the data is exposed

This is the easy end of the scraping spectrum, and it’s worth being honest about that:

• Arbeitnow runs an official public job-board API that returns the listings as JSON. No login, no API key, no cookies.
• A single HTTPS request fetches the feed. There is no Akamai, no Cloudflare challenge, no TLS fingerprinting to defeat.
• The “scraping” is really filtering and normalization done client-side: keyword matching across title/company/description, remote-only filtering, employment-type selection, tag filtering, location substring matching, and a posted-since threshold.

So why use a scraper at all instead of just hitting the feed yourself? Two reasons: the feed is paginated and unfiltered (you get everything, then filter locally), and the value is in the scheduling and delta logic — capturing only new jobs since the last run so you build an incremental, deduplicated feed rather than re-ingesting the whole board daily. The posted-since filter plus a daily schedule is the whole pattern.

Feed and filter structure

The actor wraps the official feed and exposes filters that run after the fetch:

# The upstream is a single JSON feed request, then filters apply:
keyword         -> matches across title, company, description
remote          -> true/false
employment_type -> full_time | part_time | contract | internship
tags            -> ["react", "visa", "english-speaking"]
location        -> substring match, e.g. "Berlin", "Wien"
posted_since    -> only jobs newer than N hours/days
max_results     -> cap the run

The recommended production pattern: schedule daily, set posted_since to roughly your run interval, and you get a clean stream of only-new jobs without re-processing the whole board.

▶ Run the Arbeitnow Jobs Scraper — filter DACH and EU-remote jobs by keyword, remote, type, tags and city. Schedule with posted-since for a fresh daily feed. Pay per dataset item.

Build it yourself vs. use a managed actor

Because the feed is public, building this is genuinely easy — and that’s worth saying plainly:

• Building from scratch — a few hours: fetch the feed, filter, normalize timestamps, dedup against your store. Maintenance is low because the API is stable.
• Using a managed actor — minutes: configure filters, set a schedule, point the output at your warehouse or a downstream actor. The dedup and posted-since delta logic are built in.

For this board, the honest answer is: if you only need a one-off pull, you could DIY it in an afternoon. The managed actor earns its keep when you want it scheduled, deduplicated, and feeding a pipeline without you maintaining cron jobs and a dedup table. The pricing is price-per-dataset-item, so you pay for the rows you keep.

Schema design for downstream use

A clean per-job row for a recruiter-sourcing or aggregator pipeline:

{
  "job_id": "senior-react-engineer-acme-berlin",
  "title": "Senior React Engineer",
  "company": "Acme GmbH",
  "location": "Berlin",
  "country": "DE",
  "remote": true,
  "employment_type": "full_time",
  "tags": ["react", "typescript", "visa", "english-speaking"],
  "description": "We are hiring a senior frontend engineer...",
  "apply_url": "https://www.arbeitnow.com/jobs/senior-react-engineer-acme-berlin",
  "posted_at": "2026-05-25T08:30:00Z",
  "scraped_at": "2026-05-26T06:00:00Z"
}

Schema choices worth making:

• Dedup on job_id/slug, not title — the same role gets reposted with identical titles.
• Keep tags as an array. It’s your filter for visa/relocation roles and your skill-trend signal in one field.
• Store both posted_at and scraped_at. The gap tells you how fresh the board is and lets you reconstruct when a job first appeared.
• Normalize remote to a strict boolean. The upstream sometimes encodes it inconsistently; pin it on ingest.

Typical use cases

• DACH job feeds — continuously refreshed listings across Berlin, Munich, Hamburg, Frankfurt, Cologne, Stuttgart, Vienna and Zurich, plus EU-remote.
• Relocation / visa-sponsorship services — filter on the visa and english-speaking tags to surface roles that hire internationally.
• Job aggregator products — power a German-market or EU-remote section using Arbeitnow as an upstream feed.
• Tech-market analytics — city-level hiring demand, skill trends, remote share, employment-type breakdown over time.
• Workforce planning — EU-headquartered companies monitoring supply-side hiring and role concentration.
• Lead generation — vendors selling to DACH hirers (EOR/PEO, payroll, recruiting, benefits, dev tools) sourcing companies that are actively hiring.
• Individual job hunting — international candidates running tight keyword/location/remote filters for German and EU-remote roles.

Cost math

With price-per-dataset-item billing, you pay only for jobs you actually keep. A daily delta run capturing, say, 100–300 genuinely new DACH/remote jobs is a trivial monthly cost. Because there’s no headless browser and no proxy bandwidth (the feed is open), the compute is minimal too. The economics here are about as friendly as scraping gets — the only thing you’re really buying is not having to run and babysit the scheduling and dedup yourself.

Common pitfalls

• Re-ingesting the whole board. Without posted_since you’ll pull the same jobs every run and bloat your store. Use the delta filter.
• Substring location matching surprises. “Berlin” matches “Berlin” but also potentially “Berlingen”-style edge cases; tighten with country plus tag filters if precision matters.
• Tag reliance. Visa/relocation tags depend on the employer tagging correctly — treat them as a strong hint, not a guarantee, and spot-check the description.
• Description cleaning. Some postings embed odd whitespace or leftover markup; normalize before indexing for search.
• Single-region scope. Arbeitnow is DACH/EU-focused. Don’t expect US or APAC coverage — pair it with region-specific boards if you need global reach.

Wrapping up

Arbeitnow is a refreshingly honest job board: a public JSON feed, no bot wall, clean fields. The value of a scraper here isn’t beating anti-bot defenses — it’s the scheduling, the posted-since delta logic, and the dedup that turn a raw feed into a fresh, incremental DACH/EU-remote jobs pipeline. If you need a one-off pull, you can build it in an afternoon. If you want it running daily and feeding a product without you maintaining the plumbing, a managed actor is the cleaner path.

▶ Open the Arbeitnow Jobs Scraper on Apify — Europe and remote jobs, filtered and scheduled. Pay per dataset item, start on Apify’s free monthly credit.