SEEK Jobs Scraper: Australian Job Listings in Python

SEEK is the dominant job board in Australia and New Zealand, which makes it the default dataset for anything involving the Australian labour market: salary benchmarking, hiring demand by region, competitor headcount signals, or building a niche job aggregator.

There is no open SEEK API for reading listings. The partner APIs exist for posting jobs and for approved ATS integrations, not for pulling the board. So the practical options are a browser automation script you maintain yourself, or a scraper that already handles pagination and deduplication.

Try it live: SEEK Jobs Scraper: Australia Job Listings API. Pay per result delivered. Failed and empty results are never charged.

The SEEK Jobs Scraper queries SEEK’s own search backend rather than rendering pages, so it returns structured JSON in seconds. A recent run pulled 41 listings in under eight seconds.

What you get per listing

Field	Notes
title	Job title as advertised
company	Advertiser name, absent on confidential listings
location	Suburb and state as SEEK classifies it
salary_min, salary_max	Parsed from the advertised range where present
salary_text	The raw advertised string, so you can audit the parse
work_type	Full time, part time, contract, casual
listed_at	Publication date
apply_url	Direct link to the listing
keyword, where	The query that produced the row, useful when you run many

Filtering

The four filters map onto SEEK’s own search parameters:

• keywords: an array, so one run can cover several roles
• where: a location string such as Sydney NSW, Melbourne VIC or All Australia
• workType: restrict to full time, contract and so on
• salaryMin and salaryMax: an advertised salary band

Passing several keywords in one run is cheaper than several runs, and results are deduplicated on SEEK’s own job id across the whole run, so a listing matching two of your keywords is delivered and charged once.

A salary benchmarking example

from apify_client import ApifyClient
from statistics import median

client = ApifyClient("YOUR_APIFY_TOKEN")

run = client.actor("themineworks/seek-jobs-scraper").call(run_input={
    "keywords": ["data engineer", "analytics engineer"],
    "where": "Sydney NSW",
    "workType": "full time",
    "maxJobsPerKeyword": 200,
})

mids = []
for job in client.dataset(run["defaultDatasetId"]).iterate_items():
    if job.get("_type") == "summary":
        continue
    lo, hi = job.get("salary_min"), job.get("salary_max")
    if lo and hi:
        mids.append((lo + hi) / 2)

print(f"{len(mids)} listings with advertised salary")
print(f"median advertised midpoint: ${median(mids):,.0f}")

Only a subset of listings advertise a salary, so report the count alongside the median. A median drawn from eleven listings is a very different claim from one drawn from three hundred.

Where it stops

The actor stops a keyword after two consecutive pages with nothing new, and caps at 100 pages. That matters because SEEK’s search does not usefully paginate into the deep tail: past a few thousand results the same listings recycle. Stopping early is what keeps a run fast and keeps you from paying for duplicates.

Pricing

Pay per job delivered, from $0.70 per 1,000 on higher Apify plans and $0.85 per 1,000 on the free plan. Duplicates are filtered before billing, and a keyword that returns nothing is never charged.

Use it from an AI agent

https://mcp.apify.com/?tools=themineworks/seek-jobs-scraper

An agent can then answer questions like “what does a mid level data engineer in Melbourne advertise at” directly.

Related

• LinkedIn Jobs Scraper for the same roles on LinkedIn
• Indeed Scraper for broader coverage outside SEEK’s markets
• ATS Jobs Scraper to read employer career pages directly