Collecting job listing data from Doda, one of Japan's largest job platforms, by hand is slow and breaks down quickly once you need more than a handful of records. Each company card on Doda surfaces multiple job references, salary structures, and linked detail pages, and copying that manually across hundreds of pages is not a realistic workflow. This guide walks through exactly how to extract that data using the Minexa.ai Chrome extension, no code required, starting from the c

Every job listing on Freshersworld contains more structured data than it appears to hold at first glance. Behind each card sits a salary range, a relative posting timestamp, a numeric job ID embedded in a CSS class, and a full list of eligible qualifications packed into a single traversable array. Getting all of that out cleanly, at scale, without writing selectors or maintaining parsing logic, is exactly what the Minexa API is built for. This guide walks through the complete

Michael Page publishes hundreds of active job listings across London every day. If you want that data in a spreadsheet, structured and ready to analyse, the Minexa.ai Chrome extension gets you there without writing a single line of code. This walkthrough covers the full extraction process from the michaelpage.co.uk/jobs/london page, showing exactly what you see at each step and what to do. Watch the full tutorial first The video below covers the complete workflow from install

Job market data from Instahyre is genuinely useful: role titles, required skills, company profiles, founding years, and direct application links all sitting on a structured listing page. The challenge is getting it out in a form you can actually work with at scale. This guide walks through how to extract that data programmatically using the Minexa API, covering the full workflow from training a scraper in the browser to calling the API and receiving clean, structured JSON. Wh

A Greenhouse job board page looks clean in a browser. Open the raw source and it is a different story: nested divs, dynamic class names, carousel wrappers, and image paths buried inside attributes. Getting that into a spreadsheet manually takes far longer than it should. This guide shows how to go from that raw page to a structured, exportable dataset using Minexa.ai, a Chrome extension that detects and extracts data from any web page without requiring any code. What the page

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 ex

If you are building a job market intelligence pipeline for the Canadian market, Simply Hired is a solid data source. The simplyhired.ca Toronto listings page surfaces entry-level roles across dozens of industries, updated regularly. Getting that data into a structured format, reliably and at scale, is what this guide covers. The workflow uses the Minexa API: train a scraper once using the Minexa Chrome extension, then call the API programmatically to extract listings whenever

Collecting job listings at scale sounds straightforward until you actually try to do it. The data is right there on the page. Hundreds of fields per listing, thousands of companies, millions of postings updated daily. The problem is not access. The problem is that getting that data into a structured, usable format requires solving a set of technical problems that compound quickly as volume increases. Anyone who has attempted to build a job data pipeline from scratch knows wha

CSR job listings on IIMJobs change frequently. Roles get posted, filled, and replaced within days. If you are building a talent intelligence pipeline, monitoring hiring trends, or tracking which companies are actively recruiting for CSR functions, manually checking the page is not a practical approach at any real scale. This guide shows how to extract structured jobs data from IIMJobs using the Minexa API, covering the full two-phase workflow: train a scraper once using the M

WorkBC publishes one of the most complete public job boards in British Columbia, covering roles across every sector, employment type, and region. For developers building labour market tools, recruitment pipelines, or regional salary datasets, that listing page is a structured data source waiting to be tapped. This guide shows how to extract that data at scale using the Minexa API. Train a scraper once via the Chrome extension, then call the API programmatically against any vo

Collecting job market data from Jora manually is slow, inconsistent, and breaks the moment you need more than a handful of listings. The Minexa API gives developers a repeatable, structured extraction pipeline that works across thousands of pages without writing a single selector. This guide covers the full workflow: training a scraper on Jora, retrieving your scraper_id, and calling the API to extract job listings at scale. What data Jora exposes Each listing on a Jora searc

VietnamWorks is one of Vietnam's largest job platforms, and its HR category page lists hundreds of active positions across recruitment, HRBP, generalist, and workforce management roles. Getting that data into a spreadsheet manually takes hours. With the Minexa.ai Chrome extension, the same job takes minutes. This guide walks through every step to scrape job listings from vietnamworks.com/nhan-su-kv and export structured data with no code required. What data you get Each listi