The Best Proxycurl Alternative for Job Posting Data in 2026

Why Developers Look for Proxycurl Alternatives

Proxycurl is well-known as a LinkedIn scraping API — great for enriching person and company profiles. But a lot of developers also reach for it to fetch job listings, using the company-specific jobs endpoint. If you've hit Proxycurl's pricing ceiling, found its coverage limiting, or realized that job listing data is a fundamentally different problem from LinkedIn enrichment, this article is for you.

We'll compare Proxycurl's job data capabilities head-to-head with JobDataLake, which is designed from the ground up as a job data API.

What Proxycurl's Job Listing API Actually Does

Proxycurl's job listing endpoint fetches open positions from a company's LinkedIn page. You pass in a LinkedIn company URL, and it returns the jobs currently listed there. The data comes from LinkedIn's public pages, not from the ATS directly.

This has several important implications for anyone building on it:

• Company-scoped only: You can only query by company LinkedIn URL. There is no way to search "backend engineer jobs paying over $150k in Austin" — you have to already know which companies you care about.
• LinkedIn coverage only: If a company doesn't actively publish to LinkedIn, or uses a niche ATS that doesn't push to LinkedIn, those postings are invisible.
• No enrichment: You get the raw listing as it appears on LinkedIn — title, description, location. No salary extraction, no skills array, no seniority inference.
• Pricing at scale: At roughly $0.01 per listing, pulling 100,000 job records costs $1,000 — before enrichment or deduplication.

For use cases that require broad search, structured filtering, or enriched fields, this model doesn't fit.

The ATS-Native Advantage

Job postings originate in Applicant Tracking Systems — Greenhouse, Lever, Workday, Ashby, Rippling, and 40+ others. LinkedIn is one distribution channel for these postings, not the source.

When you pull job data from the ATS directly (or from a service that does), you get meaningful differences:

• The canonical ATS job ID — the most reliable deduplication key across syndication chains
• The full, untruncated job description before any LinkedIn formatting pass
• The direct application URL, skipping LinkedIn's redirect layer
• Better freshness — ATS postings propagate before LinkedIn indexes them

JobDataLake sources from 40+ ATS platforms directly, covering the majority of tech, finance, and enterprise job postings at their origin — not after passing through LinkedIn's filter.

Feature Comparison

• Query model: Proxycurl requires a company LinkedIn URL per request. JobDataLake supports free-text search plus structured filters: title, location, salary range, skills (AND semantics), seniority, remote type, employment type, company, and date range.
• Enrichment: Proxycurl returns raw LinkedIn content. JobDataLake provides normalized salary (in thousands — 150 = $150k), an extracted skills array, inferred seniority level, and company metadata.
• Coverage: Proxycurl covers what's on LinkedIn. JobDataLake covers 1M+ active listings from 20,000+ companies across 40+ ATS platforms including many companies that don't actively use LinkedIn Jobs.
• Deduplication: Proxycurl returns raw LinkedIn records — one per listing URL. JobDataLake deduplicates across all source platforms using ATS job IDs as primary keys, preventing the same role from appearing multiple times.
• Free tier: JobDataLake offers 500 requests/day free (or via the MCP server), with no credit card required to start.

Code Comparison: Same Goal, Different Approaches

Suppose you want software engineering jobs in San Francisco paying over $150k.

With Proxycurl, this is not a single query — you'd need a list of company URLs first, then query each one, then filter salary manually since there's no salary filter:

// Proxycurl: must know company LinkedIn URLs upfront
const companies = [
  'https://www.linkedin.com/company/stripe',
  'https://www.linkedin.com/company/anthropic',
  // ... dozens more
];
const allJobs = [];
for (const url of companies) {
  const res = await fetch(
    `https://nubela.co/proxycurl/api/v2/linkedin/company/job?linkedin_job_url=${encodeURIComponent(url)}`,
    { headers: { Authorization: `Bearer ${PROXYCURL_KEY}` } }
  );
  const { job } = await res.json();
  allJobs.push(...(job ?? []));
}
// Still need to manually parse descriptions for salary

With JobDataLake, a single request returns enriched, filtered results:

// JobDataLake: one request, structured filters
const res = await fetch(
  'https://api.jobdatalake.com/v1/jobs?title=software+engineer&location=San+Francisco&salary_min=150',
  { headers: { 'X-API-Key': process.env.JDL_API_KEY } }
);
const { jobs, found } = await res.json();
// Each job has: salary_min, salary_max, skills[], seniority, company metadata
console.log(`Found ${found} matching jobs`);

When Proxycurl Is Still the Right Tool

Proxycurl's core strength is LinkedIn profile enrichment — not job data. If your use case is enriching a CRM with specific person profiles, pulling LinkedIn company metadata for known accounts, or getting structured data from individual LinkedIn profiles, Proxycurl is purpose-built for that. These are genuinely different data products.

For job listing data specifically — broad market search, structured enrichment, freshness, and scale — a purpose-built job data API is the better fit.

Migration Path

If you currently use Proxycurl's job endpoint for a fixed list of companies, migrating to JobDataLake is straightforward:

// Before: Proxycurl per-company query
async function getJobsProxycurl(linkedinUrl: string) {
  const res = await fetch(
    `https://nubela.co/proxycurl/api/v2/linkedin/company/job?linkedin_job_url=${encodeURIComponent(linkedinUrl)}`,
    { headers: { Authorization: `Bearer ${PROXYCURL_KEY}` } }
  );
  return res.json();
}

// After: JobDataLake company search — no LinkedIn URL needed
async function getJobsJDL(companyName: string) {
  const res = await fetch(
    `https://api.jobdatalake.com/v1/jobs?company=${encodeURIComponent(companyName)}&limit=100`,
    { headers: { 'X-API-Key': process.env.JDL_API_KEY } }
  );
  const { jobs } = await res.json();
  return jobs; // Already enriched with salary, skills, seniority
}

You swap a LinkedIn URL dependency for a company name string, and gain salary normalization, skill extraction, ATS-native sourcing, and broader coverage at the same or lower cost per record.