How to scrape jobs data from Seek using the Minexa.ai extension

Seek is Australia's largest job board. If you are tracking entry-level hiring trends, monitoring which companies are recruiting in regional areas, or building a structured jobs dataset for research or analysis, the listings on Seek contain exactly the data you need. The challenge is that it sits inside job cards on a paginated search results page, not in a downloadable file.

This guide shows how to extract that data using Minexa.ai, a web data extraction tool with a Chrome extension that handles the full workflow without any code.

What the extracted data looks like

Before walking through the steps, here is a sample of what Minexa.ai pulls from the Seek entry-level jobs page for Lismore and Far North Coast NSW. Fields with the list__ and meta__ prefixes have been removed for readability:

[
  {
    "company_name": "Global 360",
    "company_names": "Jobs at Global 360",
    "job_id": "jobcard-1",
    "job_title_from_details": "Entry-Level Project Administrator",
    "employment_type": "This is a Full time job",
    "location": "Byron Bay",
    "region": "Lismore & Far North Coast NSW",
    "search_metadata": {"jobId": "92602243", "jobAdType": "SPONSORED", "sectionRank": 1}
  },
  {
    "company_name": "Buslines Group Pty Ltd",
    "company_names": "Jobs at Buslines Group",
    "job_id": "jobcard-2",
    "job_title_from_details": "Administrative Assistant",
    "employment_type": "This is a Full time job",
    "location": "Lismore",
    "region": "Lismore & Far North Coast NSW",
    "search_metadata": {"jobId": "92826229", "jobAdType": "SPONSORED", "sectionRank": 2}
  },
  {
    "company_name": "HART Services",
    "company_names": "Jobs at HART Services",
    "job_id": "jobcard-4",
    "job_title_from_details": "Program Finance Assistant",
    "employment_type": "This is a Part time job",
    "location": "South Lismore",
    "region": "Lismore & Far North Coast NSW",
    "search_metadata": {"jobId": "92913148", "jobAdType": "ORGANIC", "sectionRank": 4}
  }
]

Each row maps to one job card. The job_listing_details field returns a nested array of DOM objects covering the full card structure, including the job title anchor text, the relative job URL path, the company profile link, employment type, suburb name, and the broader region label. The search_metadata field is particularly useful: it encodes whether a listing is sponsored or organic, its rank position on the page, and the numeric job ID that can be used to construct a direct listing URL.

Install the Minexa.ai Chrome extension to follow along with the steps below.

Video walkthrough

Watch the full extraction from start to finish before going through the annotated steps:

https://www.youtube.com/watch?v=HcxrBcwyb5w

The video covers every decision point in the workflow, including pagination detection and scraping mode selection.

Step-by-step extraction on Seek

Open the Minexa.ai home page to get started. The extension icon will be visible in your Chrome toolbar once installed.

Navigate to the Seek entry-level jobs page for your target region. In this example, the URL is the Lismore and Far North Coast NSW listing page. Once the page has fully loaded, open the Minexa.ai extension.

The extension popup opens and detects the current page. Click 'I'm on the right page' to confirm the target URL and move to the next step.

Minexa.ai scans the page for pagination controls and shows what it detected. For Seek, the next page button is identified automatically. Review the result and click 'Continue' to proceed.

Choose whether to scrape the listing page only, or the listing page plus each individual job detail page. For building a broad jobs dataset, the single list option is sufficient and faster to set up.

Select 'Simple' scraping mode unless you need custom click sequences or dynamic interactions. For Seek search results, simple mode captures all visible job card data without additional configuration.

Minexa.ai highlights the full job listings container on the page automatically. This is the parent element wrapping all job cards. Confirm the selection to let the extension identify all data points within it.

After clicking 'Create Scraper', all extracted columns become visible in the extension panel. Use the next and previous navigation to review each data point before finalising.

The scraping job summary screen shows scheduling and Google Sheets integration options. From here you can run the job immediately or set it to repeat on a schedule to keep your dataset current.

Once the job runs, extracted data appears in a table view. When the job finishes, you can export the full dataset as Excel or JSON directly from the results screen.

What you can do with this data

A structured Seek dataset opens up practical analysis that is not possible when browsing the site manually. Tracking which companies are consistently posting entry-level roles in a specific region, comparing sponsored versus organic listing volume over time, or identifying which suburbs within a region have the highest hiring activity are all straightforward once the data is in a spreadsheet or database.

The search_metadata field makes it possible to distinguish paid placements from organic results in the same dataset, which adds a layer of signal that is not visible when reading listings one by one.

Get started with Minexa.ai to run your first Seek extraction in a few minutes.

For a related walkthrough on extracting jobs data from another platform, see how to scrape jobs data from FlexJobs using the Minexa API.