Wantedly Scraper — Japan startup & tech jobs, hiring companies, applicant counts

Get clean, structured job data from Wantedly — Japan's largest startup and tech recruiting platform, with 180,000+ live posts from early-stage startups to listed companies. Search any keyword in English or Japanese, filter by hiring type, region and occupation, and download the results as JSON, CSV or Excel. No login, no browser, no proxy.

What you get

Run it with the default input and each row looks like this (texts shortened here):

{
  "url": "https://www.wantedly.com/projects/2539114",
  "title": "Claude Code×Copilot標準装備のAI-native開発環境",
  "lookingFor": "フルスタック/開発リーダー候補",
  "occupationCategory": "システムエンジニア",
  "occupationGroup": "エンジニアリング",
  "companyName": "株式会社リチェルカ",
  "companyUrl": "https://www.wantedly.com/companies/company_6358176",
  "companyLogoUrl": "https://d2v9k5u4v94ulw.cloudfront.net/.../108215f1-9e52-43a6-97bc-d6786c...",
  "location": "東京都港区芝浦4-11-17 中野スプリングビル 6階",
  "prefecture": "東京",
  "latitude": 35.6415596,
  "longitude": 139.7471618,
  "hiringTypes": ["Mid-career"],
  "hiringTypesJa": ["中途"],
  "employmentType": "Mid-career",
  "featureTags": ["オンライン面談OK", "話したい人を指名できる"],
  "publishedAt": "2026-08-20T13:44:51.624Z",
  "lastEditedAt": "2026-08-20T13:06:51.630Z",
  "status": "OPEN",
  "applicantCount": 0,
  "bookmarkCount": 0,
  "pageViews": 9,
  "description": "■ 募集の背景\n日本の製造業・流通業のサプライチェーンは、いまだにFAX・電話・紙帳票が中心の世界です…",
  "whatWeDo": "受発注領域を最適化するAgentic ERP「RECERQA」を開発・提供…",
  "whyWeDo": "企業間の受発注業務は、あらゆる産業の根幹を支えるオペレーションです…",
  "howWeDo": "◉ AIネイティブな開発：Claude Code・GitHub Copilotを全エンジニアが日常的に活用…",
  "salaryText": "",
  "scrapedAt": "2026-08-21T07:12:28.894Z"
}

44 fields per post by default — including the full こんなことやります description and the company's What / Why / How texts, applicant, bookmark and page-view counts, the office address with coordinates, hiring types in both English and Japanese, and Wantedly's feature tags — all from a single listing request, no detail-page visits needed.

Turn on Include detail page data and each post also gets:

• Disclosed pay — Wantedly hides salary from the listing, but many posts carry a statutory disclosure block (職業安定法). Where it exists you get salaryMin / salaryMax / salaryPeriod (year, month, hour) as numbers in JPY plus the original text, e.g. 年収 4,000,000円〜4,800,000円. In our test runs roughly half of mid-career posts had numbers.
• Legal employer details — legalEmployerName, legalWorkDuties, legalWorkLocation, legalAddress
• Company facts — companyWebsite, companyFoundedOn, companyEmployeeCount, companyAddress, companyFounder, companyIndustries
• Skill tags — categoryTags such as ["Python", "AWS", "MySQL", "フロントエンド"]
• Team — memberCount, memberPositions, and members (name, position, leader flag) as shown on the post

Who uses this

• Recruiters & sourcing teams — find which Japanese startups are hiring for which roles right now, and how many people have already applied
• Lead generation — every row carries the hiring company, its Wantedly profile, logo, office address and (in detail mode) website, founding date, headcount and founder
• Salary & market research — the only public source of numeric pay for a large slice of Japanese startup roles, already parsed into salaryMin / salaryMax
• Job boards & alert bots — sort by recent, run it on a schedule, diff against yesterday's dataset
• Competitive intelligence — watch a competitor's hiring (filter by occupation and area), read their What / Why / How messaging, track applicantCount and pageViews over time

How to use

1. Enter a keyword — python, React, データサイエンティスト, PM all work; separate several with commas
2. Optionally narrow by hiring type (new graduate / internship / mid-career / side job / freelance / part-time), area (Tokyo, Osaka, Nagoya, Kyoto, Fukuoka, Sapporo and regions), occupation (11 groups, 50 roles) and feature tags (e.g. raised 100M+ yen, online interview OK)
3. Pick a sort order — recent for monitoring, popular for the most applied-to posts
4. Set Max jobs and, if you want pay and company facts, enable Include detail page data
5. Run, then download from the Dataset tab as JSON / CSV / Excel, or pull it through the Apify API

Example input

{
  "keyword": "python",
  "hiringTypes": ["mid_career"],
  "areas": ["tokyo"],
  "occupationType": "jp__web_engineer",
  "order": "popular",
  "maxItems": 200,
  "includeDetails": true
}

Output fields

Field	Description
url, id	Post URL and Wantedly project id
title, lookingFor	Post title and the role line shown under it
occupationCategory, occupationGroup	Wantedly's occupation taxonomy (e.g. Web engineer / Engineering)
companyName, companyId, companySlug, companyUrl, companyLogoUrl, companyTwitter	Hiring company
location, locationLine1, locationLine2, prefecture, latitude, longitude, country	Office address
hiringTypes, hiringTypesJa, hiringTypeCodes, employmentType	New graduate / Internship / Mid-career / Side job / Freelance / Part-time
featureTags, featureTagCodes	Wantedly listing tags (online interview OK, students welcome, funded 100M+ yen…)
publishedAt, lastEditedAt, status	ISO timestamps and lifecycle state
applicantCount, bookmarkCount, supporterCount, pageViews	Engagement counters as shown on the post
coverImageUrl	Cover image
description, whatWeDo, whyWeDo, howWeDo	Full plain-text body and the company's What / Why / How sections
salaryDisclosed, salaryText, salaryMin, salaryMax, salaryCurrency, salaryPeriod, salaryType, salaryNote	Pay — empty unless the post carries a disclosure block (detail mode)
legalEmployerName, legalWorkDuties, legalWorkLocation, legalAddress	Statutory disclosure block (detail mode)
companyWebsite, companyFoundedOn, companyEmployeeCount, companyAddress, companyFounder, companyIndustries, companyTags, companyFeatureTags	Company facts (detail mode)
categoryTags	Skill / topic tags (detail mode)
memberCount, memberPositions, members	Team members shown on the post (detail mode)
scrapedAt	When the row was collected

Why this scraper

• Complete fields, one request — reads the same structured search API the Wantedly site renders from, so 100 posts with descriptions, counts and addresses arrive in a single call instead of one page load per post
• Parsed salary — the disclosure block is turned into sortable numbers; other Wantedly scrapers return no pay at all
• Works in Japanese and English — the same keyword box the site uses, so 機械学習 and machine learning both work
• Cheap to run — HTTP only, 1 GB of memory is plenty, no residential proxies
• Polite & resilient — paces requests and backs off automatically on 429 / 5xx, refreshes its guest token when it expires

Limitations

• Salary is only available where the company disclosed it. Wantedly does not show pay on listings by design; posts without a disclosure block return empty salary fields rather than a misleading 0
• 1,000 results per search. Wantedly stops serving results past the 1,000th for any query. Split large exports with hiring type, area or occupation filters
• Posts are Japan-focused; the country field is included in case Wantedly's other markets appear
• Counters (applicantCount, pageViews) are the public numbers shown on the post at scrape time

Legal

This Actor collects only publicly available job posts and company information that Wantedly shows to anonymous visitors. You are responsible for using the data in compliance with Wantedly's terms and applicable law, including personal-data rules for the team member names shown on posts.