Scrape Seek Jobs for Australia & NZ Recruiting Data (2026)

Thirdwatch's Seek Jobs Scraper returns Seek listings across Australia (seek.com.au) and New Zealand (seek.co.nz) — title, company, location, salary, classification, work type, work arrangement, teaser, bullet points, and optional full descriptions. Built for ANZ recruiter agencies, in-house TA teams, salary-research analysts, and aggregator builders who need Seek's catalog as a structured feed, no API key or login required.

Why scrape Seek for Australia and NZ recruiting

Seek is the dominant job board across Australia and New Zealand. According to the Australian Bureau of Statistics Labour Force release, Australia has roughly 14 million people in the labour force, and Seek carries the deepest local listing coverage of any single source for that market. For any ANZ-focused pipeline — recruiter agency, in-house talent-acquisition team, salary-research analyst — Seek is the primary source. The blocker for systematic access: Seek does not offer a public job-search API for third-party developers.

The job-to-be-done is structured. An ANZ recruiter agency wants a daily refresh of every senior software engineer role across Sydney, Melbourne, and Brisbane. A people-analytics team benchmarks compensation by classification and work arrangement across Australia and New Zealand. A staffing firm placing nurses across regional health systems tracks Healthcare & Medical listings filtered by work type. A job-board aggregator ingests Seek alongside LinkedIn and Indeed for the broadest possible ANZ coverage. All of these reduce to keyword + location + siteKey pulls returning structured rows ready for pandas, BigQuery, or a recruiter CRM.

How does this compare to the alternatives?

Three options for getting Seek data into a pipeline:

A DIY build has to keep up with Seek's evolving search responses and rebuild field mapping on every change. A generic scraping API returns raw HTML you still have to parse into Seek's classification and salary structure. The Seek Jobs Scraper actor page returns clean, named fields out of the box — title, company, salary, classification, work type, and work arrangement — across both the AU and NZ sites.

How to scrape Seek for ANZ recruiting in 4 steps

Step 1: How do I authenticate against Apify?

Sign in at apify.com (free tier, no credit card), open Settings → Integrations, and copy your personal API token. Every example below assumes the token is in APIFY_TOKEN:

export APIFY_TOKEN="apify_api_xxxxxxxxxxxxxxxx"

Step 2: How do I pull jobs across roles and cities?

Pass search terms in keywords, set where to a location label, and choose siteKey for the country. Each keyword returns up to maxResults jobs.

import os, requests, pandas as pd

ACTOR = "thirdwatch~seek-jobs-scraper"
TOKEN = os.environ["APIFY_TOKEN"]

resp = requests.post(
    f"https://api.apify.com/v2/acts/{ACTOR}/run-sync-get-dataset-items",
    params={"token": TOKEN},
    json={
        "keywords": ["software engineer", "data analyst", "registered nurse"],
        "where": "Sydney NSW",
        "siteKey": "AU-Main",
        "scrapeDescription": False,
        "maxResults": 100,
    },
    timeout=900,
)
df = pd.DataFrame(resp.json())
print(f"{len(df)} Seek jobs across {df.company.nunique()} companies")

Three keywords × 100 results = up to 300 listings — small enough to run on demand at the actor's transparent pay-per-result pricing. To cover New Zealand, run the same input with "siteKey": "NZ-Main" and a NZ location like "Auckland".

Step 3: How do I filter by salary, classification, and work arrangement?

salary is present only when the advertiser publishes it, so filter to non-null rows before any band analysis. classification and work_arrangement are clean structured fields.

disclosed = df[df.salary.notna() & (df.salary != "")]
print(f"Salary disclosed on {len(disclosed)}/{len(df)} listings "
      f"({len(disclosed)/len(df):.0%})")

# Volume by Seek classification (e.g. Information & Communication Technology)
by_class = df.classification.value_counts()
print(by_class.head(10))

# Remote / hybrid demand from the structured work_arrangement field
remote = df[df.work_arrangement.isin(["Remote", "Hybrid"])]
print(f"{len(remote)} remote/hybrid roles "
      f"({len(remote)/len(df):.0%} of the batch)")

work_arrangement is Seek's own On-site / Hybrid / Remote flag — far more reliable than parsing "remote" out of titles. Because salary is frequently blank, always report disclosure rate alongside any median so the numbers are honest.

Step 4: How do I enrich descriptions and push to a CRM?

For keyword-matchable body content, enable scrapeDescription, then dedupe on the stable url and upsert into your ATS or CRM.

resp = requests.post(
    f"https://api.apify.com/v2/acts/{ACTOR}/run-sync-get-dataset-items",
    params={"token": TOKEN},
    json={
        "keywords": ["registered nurse"],
        "where": "Melbourne VIC",
        "siteKey": "AU-Main",
        "scrapeDescription": True,
        "maxResults": 100,
    },
    timeout=1800,
)
df = pd.DataFrame(resp.json()).drop_duplicates(subset=["url"])

import psycopg2.extras
with psycopg2.connect(...) as conn, conn.cursor() as cur:
    psycopg2.extras.execute_values(
        cur,
        """INSERT INTO seek_jobs
              (url, title, company, location, salary, work_type,
               work_arrangement, classification, listing_date, scraped_at)
           VALUES %s
           ON CONFLICT (url) DO UPDATE SET
             salary = EXCLUDED.salary,
             scraped_at = now()""",
        [(j["url"], j["title"], j["company"], j.get("location"),
          j.get("salary"), j.get("work_type"), j.get("work_arrangement"),
          j.get("classification"), j.get("listing_date"), "now()")
         for _, j in df.iterrows()],
    )
print(f"Upserted {len(df)} Seek jobs")

Schedule the actor on Apify's scheduler at daily cadence and the loop is self-maintaining — fresh Seek roles land in the recruiter's pipeline every morning.

Sample output

A single Seek record looks like this. Five rows of this shape weigh roughly 8 KB without descriptions, more once scrapeDescription is enabled.

{
  "id": "84231055",
  "title": "Senior Software Engineer",
  "company": "Atlassian",
  "location": "Sydney NSW",
  "locations": ["Sydney NSW", "Remote"],
  "salary": "$150,000 - $180,000 per year",
  "work_type": "Full time",
  "work_types": ["Full time"],
  "work_arrangement": "Hybrid",
  "classification": "Information & Communication Technology",
  "subclassification": "Engineering - Software",
  "teaser": "Join a platform team building developer tooling at scale.",
  "bullet_points": ["Flexible hybrid work", "Equity + bonus", "Modern stack"],
  "listing_date": "2026-06-18T03:12:00Z",
  "listing_date_display": "2d ago",
  "is_featured": false,
  "url": "https://www.seek.com.au/job/84231055",
  "keyword": "software engineer",
  "scraped_at": "2026-06-20T08:00:00Z"
}

url is the canonical natural key for upsert. salary is whatever Seek displays publicly and is frequently null when the advertiser withholds it. classification and subclassification follow Seek's own taxonomy — high-signal for cross-role analysis without keyword guessing. bullet_points is a clean array of the listing's highlighted selling points, and description (not shown above) is populated only when scrapeDescription is enabled.

Common pitfalls

Three things go wrong in production Seek pipelines. Salary opacity — many Seek advertisers do not publish pay, so salary is null on a large share of listings; pipelines that average without filtering null rows produce wildly wrong estimates, so always filter to disclosed-salary rows and report the disclosure rate. AU vs NZ mixing — AU-Main and NZ-Main are separate sites; run them as separate batches with the matching where location, and dedupe across the combined dataset by url. Description cost-vs-depth — the teaser and bullet points are always returned, but full body text only arrives when scrapeDescription is enabled, which adds a per-job fetch; leave it off for high-volume sweeps and on only when you need matchable description content.

Thirdwatch's actor handles the request work and field mapping so you get named columns instead of raw HTML, and the schema stays stable across AU and NZ. Pair Seek with LinkedIn Jobs Scraper for international coverage and Indeed Scraper for broader volume in the same markets. A fourth subtle issue: featured listings (is_featured true) surface ahead of newer organic posts, so for true new-posting velocity sort by listing_date rather than result order. A fifth: the same role can appear under multiple location tags via the locations array when an employer hires across offices — dedupe by url before computing per-city volume so multi-location postings do not double-count.

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