Why candidate sourcing needs automation
Recruiting teams spend hours every week searching LinkedIn manually: filtering by skills, location, experience, then clicking through profiles one by one to qualify candidates.
This doesn't scale when you're hiring for multiple roles, working on time-sensitive searches, or managing high-volume recruiting pipelines.
The Z Scraper LinkedIn API on RapidAPI lets you automate candidate search and profile enrichment, so you can build sourcing pipelines that run in the background and surface qualified candidates automatically.
What you can build
A typical LinkedIn candidate sourcing pipeline includes:
Candidate search
Query LinkedIn by job title, skills, location, company
Filter by seniority, industry, years of experience
Return matching profiles with preview data
Profile enrichment
Pull full work history, education, skills for each match
Extract contact info when available
Flag candidates with recent job changes or activity
Candidate scoring
Rank candidates by skills match, location fit, experience level
Identify passive candidates (employed but open to opportunities)
Filter out competitors, irrelevant industries, or over/under-qualified profiles
Pipeline integration
Push qualified candidates into your ATS (Greenhouse, Lever, Ashby)
Export to Google Sheets for collaborative review
Trigger email sequences for outreach
Step 1: Set up the Z LinkedIn API on RapidAPI
1. Subscribe to the Z Scraper API on RapidAPI (free tier includes 100 requests/month)
2. Copy your API key from the RapidAPI dashboard
3. Test the people search endpoint in the API console
4. Review the response structure to understand available fields
Step 2: Build your candidate search query
The people search endpoint accepts filters like:
• Keywords (job titles, skills, companies)
• Location (city, country, region)
• Current company
• Past company
• Industry
Example: searching for senior product designers in San Francisco with e-commerce experience.
Step 3: Enrich candidate profiles
Once you have a list of matching profiles, enrich each one to get full details:
The enriched data gives you everything you need for candidate qualification:
• Work history (roles, companies, tenure)
• Skills and endorsements
• Education background
• Recent activity (posts, engagement)
• Contact info (when available)
Step 4: Score and filter candidates
Not every search result is a good fit. Add scoring logic to prioritize the best matches:
Required skills match
Check if the candidate's skill list includes must-have skills
Weight by endorsement count or years of experience
Location fit
Prioritize local candidates for on-site roles
Flag remote-friendly profiles for distributed teams
Experience level
Calculate total years of experience from work history
Filter by seniority (junior, mid, senior, lead, executive)
Recency signals
Prioritize candidates with recent job changes (may be open to new roles)
Surface profiles with recent LinkedIn activity (active users)
Step 5: Push candidates to your ATS or Sheets
Once you've filtered and scored candidates, push them into your recruiting workflow:
Google Sheets integration
Simple option for small teams or collaborative review
Use Google Sheets API to append rows with candidate data
Add columns for score, status, recruiter notes
ATS integration (Greenhouse, Lever, Ashby)
Most ATS platforms have APIs for creating candidate records
Push enriched profile data as a new candidate entry
Include source tag (e.g., 'LinkedIn API - Product Designer search')
Email outreach
Trigger email sequences for warm outreach (if contact info is available)
Use enrichment data to personalize messaging
Track responses and update candidate status
Real workflow: Sourcing 100 senior engineers in 20 minutes
Here's a real-world sourcing pipeline example:
1. Run people search for 'Senior Software Engineer' + 'San Francisco' + 'React'
2. API returns 200 matching profiles
3. Enrich top 100 profiles (sorted by relevance)
4. Score by: React skills, 5+ years experience, recent job activity
5. Filter out: competitors, contractors, consultants
6. Push top 50 candidates to ATS with enriched data
7. Export top 20 to Sheets for hiring manager review
Total time: ~20 minutes, fully automated after initial setup.
This replaces days of manual LinkedIn searching and click-through research.
Common sourcing use cases
Tips for better candidate sourcing
• Use broad search terms first, then filter programmatically (faster than narrow API queries)
• Cache enriched profiles for 30-60 days to reduce API costs
• Add deduplication logic to avoid re-processing candidates
• Track sourcing metrics: search volume, enrichment success rate, candidate response rate
• Respect LinkedIn terms and candidate privacy when using data for outreach
• Build in rate limiting to stay within API quotas