How to Build a Recruiter Sourcing List
Use ProfileSpider to build recruiter sourcing lists from public team pages, directories, Google X-Ray results, speaker pages, portfolios, and profile sources.
Goal
What This Workflow Is For
Build a structured candidate sourcing list from public profile sources and export it for review, outreach, or recruiter handoff.
Use this workflow when you need to build a candidate list for a role, client, niche, skill set, company type, or location. Good examples include senior frontend engineers in Berlin, AI researchers in Amsterdam, sales leaders in SaaS, designers in London, or founders who could be hiring-manager contacts.
The flow is: find public candidate source pages → extract visible profiles → save rows to a list → review and qualify → enrich where useful → find emails per row where available → export CSV, Excel, or JSON.
Prerequisites
Before You Start
Confirm the page and tooling match this workflow.
You need:
- ProfileSpider installed in Chrome and signed in
- A target role, skill set, location, company type, or hiring brief
- One or more public source pages with visible candidates, profiles, team members, or professional listings
- A saved list name such as frontend-engineers-berlin, ai-researchers-amsterdam, or client-acme-shortlist
This workflow works best when the page has repeated visible people, profile cards, team members, search results, speaker listings, or professional directory rows.
Fit
Best For / Not Ideal For
Set expectations before you install or run an extract.
Best for
- Public company team pages
- Google X-Ray search result pages
- Professional directories
- Conference speaker and panelist pages
- Developer, portfolio, and open-source profile pages
- University lab, research group, or alumni pages
- Agency, consultancy, and competitor staff pages
- Recruiting agency candidate sourcing workflows
Not ideal for
- Pages where candidate data is hidden behind a login you cannot access
- Private, restricted, or unauthorized data sources
- PDFs, screenshots, or images of candidate lists
- Pages that only show logos or avatars without names, roles, or links
- Pages where the profiles only appear after complex interactions that are not visible before extraction
- ATS replacement workflows that need full pipeline management
Steps
Step-by-Step Workflow
- 1
Define the sourcing target
Start with a clear candidate profile. Define the role, seniority, location, skill set, industry, company type, or client brief. For example: senior frontend engineers in Barcelona, AI researchers in the Netherlands, SaaS VP Sales candidates, or agency recruiters in the UK.
- 2
Find public candidate source pages
Open public sources such as company team pages, Google X-Ray results, professional directories, conference speaker pages, portfolio lists, developer profiles, research lab pages, or public community pages. Look for pages with repeated visible people or profile links.
- 3
Load the candidate information in Chrome
Make sure the names, roles, companies, locations, profile URLs, websites, or social links you want are visible before running extraction. If the page uses tabs, filters, pagination, or load-more buttons, prepare the page first.
- 4
Run ProfileSpider on the source page
Click the ProfileSpider extension and run extraction. A normal page scrape uses 1 credit per page, regardless of how many profiles are found within your plan cap.
- 5
Save useful candidates to a sourcing list
Save relevant extracted rows to a new or existing list. Use clear names and tags such as frontend, senior, barcelona, client:acme, shortlist, needs-review, or source:xray.
Saving rows, adding tags, and adding notes do not use credits.
- 6
Review, qualify, and enrich the list
Remove irrelevant rows, add notes, mark fit status, and review duplicates inside the saved list. Use enrichment when rows include profile, website, or social URLs and you want more detail. Bulk enrichment uses 1 credit per eligible profile in that flow.
- 7
Find emails per row and export the shortlist
For individual candidates where email finding is appropriate and supported, run email finding per row. If no valid email is returned, there is no charge. Export the reviewed list to CSV, Excel, or JSON for recruiter review, hiring-manager handoff, client delivery, or outreach.
Schema
What ProfileSpider Extracts
Default fields for this workflow. Add or remove columns before you extract.
- CandidateCandidate name when visible on the source page.
- Current TitleRole, headline, job title, or professional title when available.
- Current CompanyEmployer, organization, lab, agency, consultancy, or company name when visible.
- LocationCity, region, country, or remote/location context when available.
- LinkedIn URLLinkedIn profile URL when visible or linked from the source page.
- Profile URLPublic profile, portfolio, speaker page, GitHub-style profile, author page, or personal site.
- EmailOnly filled when visible on the source page or when per-row email finding later returns a valid result.
- Source URLThe page where the candidate row came from. Keep this for verification and sourcing context.
Output
Example Output
What a downloaded file looks like. Real exports are saved as .csv, .xlsx, or .json.
| Candidate | Current Title | Current Company | Location | LinkedIn URL | Fit Status | Tags | Source URL |
|---|---|---|---|---|---|---|---|
| Sofia Martin | Senior Frontend Engineer | Northstar Labs | Barcelona, Spain | linkedin.com/in/sofiamartin | Shortlist | frontend;barcelona;reviewed | exampletech.com/team |
| Daniel Weber | VP Engineering | Weber Cloud | Berlin, Germany | linkedin.com/in/danielweber | Referral source | engineering-lead;saas | conference.example/speakers |
| Nina Verhoeven | Machine Learning Researcher | Example AI Lab | Amsterdam, Netherlands | linkedin.com/in/ninaverhoeven | Needs review | ml;research;needs-review | university-lab.example/people |
Troubleshooting
Common Problems
The page has names but no emails
That is common in sourcing. Save the rows first, then use per-row email finding where appropriate and supported. If no valid email is returned, there is no charge.
The page has profile links but little context
Save the rows, then use enrichment on eligible profile, website, or social URLs where useful. Enrichment uses 1 credit per URL opened.
The extracted list includes irrelevant people
Review and remove unrelated rows before enrichment, email finding, or export. Use tags and notes to separate shortlist, maybe, and not-a-fit candidates.
The same candidate appears twice
Some sources repeat people across teams, agendas, search results, or profile sections. Review duplicates inside the saved list before export.
The page only shows part of the candidate list
Check whether the page uses pagination, filters, tabs, or load-more buttons. Load the relevant candidates first, then run extraction.
Questions