Jora Scraper: Australian, NZ & Singapore Jobs in 2026

One search box, three job markets

Jora is a job aggregator: it gathers listings from many employers into one search, with country sites for Australia, New Zealand and Singapore. For recruiters, HR teams and labor-market analysts in those markets, it is a practical place to read who is hiring, where and, when listed, at what pay.

At scale, the format works against you: results come a page at a time, salary must be told apart from the job type, and a listing’s summary may be AI-written rather than the employer’s.

What you can extract

Per listing:

• Identity: job ID, listing URL and title
• Employer: company name and company rating
• Location: city or region, and the country site (AU, NZ or SG)
• Job details: job type, salary as its own field (such as “$85,000 - $95,000 a year”), search summary, and full description with detail scraping on
• Listing signals: posting date as shown (“Posted 12h ago”), sponsored, new-job and early-applicant flags, quick apply and search rank
• Summary origin: abstractType says whether the summary is AI-generated (extended_llm) or the employer’s original text
• Metadata: the search query that found the job and the scrape time

The technical challenge

• Result caps: Jora typically shows up to about 500 results per search query. Covering a market means several keyword or location searches, then removing overlap on job ID.
• Summary versus description: search pages carry a short summary; the full text lives on each job page, one extra request per listing.
• Relative dates: postedDate is text such as “Posted 12h ago”; convert it against scrapedAt before sorting by recency.
• Blocking: failed requests need retries; the Actor retries automatically with session rotation, and proxies are included.

DIY or a ready-made Actor?

Our Jora Scraper on Apify takes search terms with an optional city or region and a country (Australia, New Zealand or Singapore), or Jora search URLs. Limits cover total listings (maxItems, up to 10,000) and result pages per search (maxPages, up to 100), and scrapeDetails adds the full description. No login needed. Pay-per-result at about $0.001 per job.

A practical workflow

1. Split the market: one search per role, plus a location such as “Sydney NSW” or “Auckland” when the city matters; several searches fit in one run.
2. Skim first: run with detail scraping off to size volumes, salary coverage and top employers per role.
3. Go deep where it pays: rerun the searches that matter with scrapeDetails on, and flag extended_llm summaries before any text analysis.
4. Schedule and compare: rerun weekly and compare on jobId to spot new listings and employers that keep hiring; a pipeline that doesn’t break handles deduplication and history.

Use cases that pay off

• Recruitment: see which employers hire for the roles you place, city by city, across all three markets.
• Salary and demand research: job volumes and posted salary ranges by role and region; LLM-powered extraction turns salary text into numbers.
• Competitor hiring: track the roles a competitor opens and how often.
• NLP datasets: full descriptions with a flag that separates AI-written summaries from original text, for classification or trend models.

The legal boundaries

• Public listings only: the Actor reads listings any visitor sees, with no login; Jora’s terms of service apply.
• Jobs, not people: rows describe jobs and employers. If a contact name turns up in a description, Australia’s Privacy Act, New Zealand’s Privacy Act and Singapore’s PDPA apply once you store it.
• Verify before relying: salaries and ratings are what the listing shows; check the original posting before any hiring or pay decision.