PNet Scraper — South Africa Jobs, Salary & Employers

Export vacancies from PNet, South Africa's largest job board — company, location, advertised salary and description snippet — to JSON, CSV or Excel. Keyless, no login.

What does PNet Scraper do?

PNet (StepStone group) renders its results server-side and tags every field with a stable data-at attribute. This Actor parses those attributes rather than the hashed CSS class names around them, which is what keeps it working when the site restyles.

One quirk decides whether the output is usable at all: PNet inlines its CSS-in-JS rules as <style> blocks inside the job cards. A naive text extraction returns .res-1fx3snc{box-sizing:border-box…} where the job title should be. This Actor strips the style nodes before parsing, so every column holds real content.

South African salaries are written freely — R62 000 – R70 000 CTC per month, R70-R50 k pm neg, Market Related. The Actor keeps the original label, derives a sortable numeric floor that understands the k shorthand even when it sits on the second figure, and flags "Market Related" ads separately instead of inventing a number for them.

Who is it for?

• Recruitment agencies tracking who is hiring across Gauteng, Western Cape and KZN.
• Salary researchers building South African pay benchmarks.
• B2B sales teams using hiring activity as a growth signal.
• Job boards and aggregators that need a South African feed.
• Labour-market analysts measuring formal-sector demand by role.

Use cases

• Build a pay benchmark for developers or accountants across South African cities.
• List every company hiring in a discipline this week, with links to their PNet profile.
• Measure what share of ads disclose pay versus say "Market Related".
• Track posting volume by city, month over month.
• Feed a niche board with fresh South African listings.

Why use this Actor?

• Keyless — nothing to sign up for.
• 16 structured fields, parsed from stable data-at attributes.
• Sortable salary that handles k shorthand and CTC phrasing.
• Market-related flag instead of a fabricated figure.
• Company profile link for every employer that has one.
• Description snippet included, so rows are useful without opening each ad.

What data can you extract?

Field	Type	Description
jobId	string	PNet listing ID
title	string	Job title
jobUrl	string	Public ad page
companyName	string	Hiring company
companyUrl	string	Employer profile on PNet
location	string	City or region
salaryLabel	string	Salary exactly as advertised
salaryFrom	number	Numeric floor for sorting
salaryMarketRelated	boolean	Ad says "Market Related"
currency	string	ZAR when a figure is present
workType	string	Remote or hybrid note, when shown
postedLabel	string	Posting recency as displayed
snippet	string	First part of the ad description
country	string	South Africa
source	string	Always pnet
scrapedAt	string	Run timestamp (ISO 8601)

Example output

{
  "jobId": "4235035",
  "title": "Specialist: Developer (Permanent)",
  "jobUrl": "https://www.pnet.co.za/jobs--Specialist-Developer-Permanent-Edenvale-MSP-Staffing-Pty-Ltd--4235035-inline.html",
  "companyName": "MSP Staffing Pty Ltd",
  "location": "Edenvale",
  "salaryLabel": "R62 000 – R70 000 CTC per month",
  "salaryFrom": 62000,
  "salaryMarketRelated": false,
  "currency": "ZAR",
  "postedLabel": "2 days ago",
  "country": "South Africa",
  "source": "pnet"
}

How to use it

1. Enter a keyword, or leave it empty to walk the full listing.
2. Optionally filter by location.
3. Switch on Only ads with a salary figure when building a benchmark.
4. Set Maximum results and run, then export JSON, CSV or Excel.

Input example

{
  "query": "accountant",
  "location": "Cape Town",
  "withSalaryOnly": true,
  "maxResults": 1000
}

Notes and limits

• Roughly 2 in 5 ads publish a salary; many say "Market Related". Those rows keep salaryLabel but leave salaryFrom as null, with salaryMarketRelated set to true.
• Recruitment agencies post a large share of South African ads, so companyName is often the agency rather than the end employer — that is how the ad is published.
• PNet renders 25 cards per page; the Actor stops when a page returns noticeably fewer, meaning the listing is exhausted.
• snippet is the opening of the description, capped for size. Open jobUrl for the full text.
• You are responsible for how you use employer data.

FAQ

Do I need a PNet account? No. Listings are public and this Actor needs nothing from you.

Why is the salary missing on many rows? Because the ad says "Market Related". The flag tells you which, rather than the Actor guessing a number.

Does it handle "R70-R50 k pm"? Yes. The k multiplier is read from the whole label, so that becomes 70 000 rather than 70.

Is the company the real employer? Sometimes it is an agency; PNet publishes whichever posted the ad and this Actor reports it faithfully.

How am I charged? Pay per result — you pay for the vacancies delivered.

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*Maintained: last verified 2026-09-01 — checked end-to-end against live run history (reliability, output fields and types, and time/memory budgets).

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Is it legal?

This Actor collects only publicly available data — the same content any visitor to PNet can see without logging in. It does not bypass authentication and does not gather private information. How you use the output is your responsibility: respect the source's terms, applicable copyright, and data-protection law such as GDPR where personal data is involved.

Related scrapers

Other Actors from the same collection:

• Platsbanken
• Jobberman
• Jobs.cz
• Hirist.tech IT Jobs

Scheduling and integrations

Run this Actor on a schedule — hourly, daily or weekly — and have every run append to the same dataset, which is how you turn a one-off export into a time series. Results can be pushed straight into Google Sheets, Slack, Zapier, Make, Airtable or any Webhook.

How much PNet data can you extract in one run?

A single run returned 897 job ads in 251 seconds on the example input. That is roughly 214 job ads a minute. Widen the search — more queries, more pages, a broader location — and the same run goes deeper.

There is no hard ceiling built into this Actor. What bounds a run is the result limit you set, the breadth of the input you give it, and how much PNet itself publishes. Because billing is per result, a wider run costs proportionally more and a narrow one costs almost nothing — so it is worth starting small and scaling once the output looks right.

PNet scraping — frequently asked questions

Do I need a PNet API key, account or login?

No. This Actor reads publicly available pages and feeds, so there is nothing to register for and no credentials to manage.

How do I export PNet data to CSV, Excel, JSON or Google Sheets?

Open the Storage tab after a run and pick the format you want — CSV, Excel, JSON, XML or an HTML table. For Google Sheets, Airtable or a database, use the dataset URL or the Apify API so the sheet refreshes itself on every run.

Can I schedule this PNet scraper to run daily or hourly?

Yes. Use the Schedules tab and pick a cadence. Daily is enough for most tracking work; pick hourly only if the job ads you care about really do change that fast.

How many job ads can I get, and what does it cost?

See the volume section above for a measured figure. Billing is per result, so you pay for the rows you actually receive and a run that finds nothing costs nothing.

Is scraping PNet legal?

This Actor collects only information that PNet publishes publicly. It does not log in, does not defeat access controls and does not touch private data. You remain responsible for how you use the output, including under GDPR and similar rules where personal data is involved.

What happens if PNet blocks the run or changes its layout?

A refused exit IP is swapped for a fresh one within a quarter of a second and retried, and rate limiting is waited out rather than fought. If nothing gets through, the run fails with the reason instead of reporting success with zero rows — so an empty result always means "no matches", never "the source would not answer".

Can I use this with an AI agent, LangChain or an MCP client?

Yes. The Actor is callable over the Apify API and through Apify's MCP server, so an agent can run it and read the dataset back as a tool call.