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Can Yılmaz
Can Yılmaz

Posted on • Originally published at apify.com

Scraping Himalayas Remote Jobs for recruiters: what data is available and how to use it

#webscraping #apify #jobs #productivity

If you are working in the recruiters space and you have ever needed Himalayas Remote Jobs as a structured feed, you know the gap between "the data exists on a website" and "the data is in my notebook" can swallow a whole sprint. Here is what the dataset actually contains and the workflow I would build around it.

Why this data matters for recruiters

The short version: tracking hiring trends, building talent pipelines, salary benchmarking and competitive recruiting intelligence. Himalayas Remote Jobs Scraper 100,000+ Remote Jobs Worldwide Scrape remote job listings from Himalayas (himalayas.app), one of the largest remote-work job boards with 100,000+ remote jobs, straight from its public API. For recruiters, talent-intel analysts and job-market researchers, the value is having a normalised, queryable representation of a source that ordinarily fights structured access.

Fields available

The dataset comes back with these fields per record:

  • title -- title
  • company -- company
  • companySlug -- company slug
  • companyLogo -- company logo
  • employmentType -- employment type
  • seniority -- seniority
  • categories -- categories
  • parentCategories -- parent categories
  • minSalary -- min salary
  • maxSalary -- max salary
  • currency -- currency
  • locationRestrictions -- location restrictions
  • timezoneRestrictions -- timezone restrictions
  • excerpt -- excerpt
  • description -- description
  • url -- url
  • postedAt -- posted at
  • expiresAt -- expires at
  • guid -- guid
  • scrapedAt -- scraped at

The mix is decent. You get enough identifying information to deduplicate across runs, enough content to actually answer questions, and enough timestamps to do time-series work.

Two example records

Trimmed for readability:

{
  "title": "Business Development Manager – Enterprise Team",
  "company": "KnowledgeBrief",
  "companySlug": "knowledgebrief",
  "companyLogo": "https://cdn-images.himalayas.app/htk59y2g3qaksdcowvhv1elbhata",
  "employmentType": "Full Time",
  "seniority": [
    "Manager"
  ],
  "categories": [
    "Enterprise-Business-Development-Manager",
    "Enterprise-Sales-Development-Manager",
    "... (2 more)"
  ],
  "parentCategories": [
    "Sales"
  ],
  "minSalary": 30000,
  "maxSalary": 40000
}
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{
  "title": "Biologist with Python Experience - Freelance AI Trainer",
  "company": "Mindrift",
  "companySlug": "mindrift",
  "companyLogo": "https://cdn-images.himalayas.app/xq3hn9b4xx58golfhgf8twc4izd7",
  "employmentType": "Contractor",
  "seniority": [
    "Mid-level"
  ],
  "categories": [
    "AI-Training-Data-Creation",
    "Computational-Biology",
    "... (3 more)"
  ],
  "parentCategories": [],
  "minSalary": 158080,
  "maxSalary": 158080
}
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A recruiter could start asking real questions on day one with this shape: aggregate counts across categorical fields, distributions on numeric fields, simple text analysis on the long-form content.

A workflow that works

If I were dropping this into an existing recruiters stack:

  1. Schedule a recurring scrape. Daily or every few hours depending on how fast the source updates.
  2. Land it raw. Object storage, partitioned by date. Cheap, replayable, future-proof against schema changes.
  3. Curate. Dedup on the natural key, type-cast the columns, surface the curated view to your dashboard or notebook layer.
  4. Layer enrichment. Most recruiters workflows need a second source -- reference data, internal CRM, third-party signal -- to extract real value. Build that join early.

Honest trade-offs

This is not a magic dataset. Things to know up-front:

  • The source can rate-limit you. Plan for retries and back-off.
  • Free-text fields are noisy. Budget for cleaning.
  • Schema can drift if the source redesigns. Wire up assertions on record counts and key presence.

Concrete questions you could answer day one

A recruiter working with this dataset could, on the first day:

  • Rank entities by any numeric field, broken down by a categorical field, to find leaders and laggards.
  • Build a time-series of new entries per day from the timestamp columns to see growth or decline.
  • Pull the long-form text into a quick TF-IDF or topic-model to surface what the dataset is actually about under the hood.
  • Spot duplicates and near-duplicates as a data-quality exercise, which often surfaces interesting structural anomalies in the source.

None of those questions require a finished pipeline. A notebook, the JSON file, and an afternoon are enough.

Verdict

For recruiters, this is a useful input -- not a finished answer, but a strong starting point that saves you from writing a brittle HTML parser of your own. The marginal cost of trying it on a real project is a few hours; the marginal value if the dataset clicks with your workflow is open-ended.

For live, customizable extractions of this data, the actor that produced the dataset shown above is published on the Apify Store: logiover/himalayas-remote-jobs-scraper. It supports JSON, CSV and Excel exports and runs on a schedule.

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