Reverse Email Lookup — the person and LinkedIn profile behind any email

$3.20 per 1,000 people found, $8 with the full 164-field document. An email we cannot match is a free row with a reason — you pay for answers, not attempts.

Paste a list of email addresses and get back structured professional profiles: name, current job title and employer, LinkedIn profile URL, bio, location, seniority, skills, education, and social links — from a database of 1B+ professional profiles.

This is a database lookup, not a live scrape. Results return in seconds, nothing is crawled on demand, and every row carries a freshness stamp (_freshness: fresh_90d, updated_1y, or older) plus the record's exact updatedAt timestamp.

Why this one

1. You pay only for found. Emails we cannot match are free not_found rows; malformed entries are free invalid rows. Several alternatives on this shelf bill every pushed row — misses included. We never do.
2. The widest card on the shelf. The default summary is already 60+ fields — identity, current role and employer, bio, skills, education, certifications, languages, seniority signals, salary estimate, social links. fullOutput: true upgrades it to the complete document — 164 fields across 37 sections — a depth this shelf simply does not offer.
3. Both address types. Work emails resolve via the employer, personal emails via the profile record — one input, one code path.

Who is this for

• Inbound qualification: who just signed up with this email?
• CRM enrichment: turn an email-only list into titled, located contacts.
• Fraud & KYB screening: check who is really behind a free-mail address.

Input

{
  "emails": ["satya.nadella@microsoft.com"],
  "fullOutput": false
}

What you get — 60+ fields per match, 164 with fullOutput

Group	Summary card (default, $0.0032/found)	Full document (fullOutput: true, $0.008/found)
Identity	full name, first/last, headline, photo, profile slug	same + background image
Current role	job title, employer name + slug (flat columns), position dates, industry	+ role descriptions, employment type, job function
Career history	current positions	every past position with dates and descriptions
Education	schools, degrees, fields of study, dates	+ descriptions, grades, activities
Skills & languages	full skills list, languages with proficiency	same
Certifications	name, authority, dates	+ license numbers and links
Bio	full "about" text	same
Location	city, state, country (ISO code + full names)	same + coordinates when known
Seniority signals	level, total experience years, tenure, decision-maker flag	same
Salary estimate	inferred min–max	same
Social links	professional profile URL, GitHub, Twitter/X, Facebook, website	same
Contacts	—	email addresses (work + personal) and phone numbers
Deep sections	—	publications, patents, awards, projects, courses, organizations, volunteer work, articles, recommendations, up to 50 related profiles
Honesty meta	_status, _freshness, updatedAt, _matchCandidates	same + per-section coverage map (_meta)

Output — a real summary row

One row per input email, aligned with your list. This is an actual row from the database (contact-free summary mode):

{
  "_status": "found",
  "_input": { "email": "satya.nadella@microsoft.com" },
  "_freshness": "fresh_90d",
  "companyName": "Microsoft",
  "companySlug": "microsoft",
  "_view": "lite-v3",
  "slug": "satyanadella",
  "fullName": "Satya Nadella",
  "firstName": "Satya",
  "lastName": "Nadella",
  "headline": "Chairman and CEO at Microsoft",
  "jobTitle": "Chairman and CEO",
  "industry": "Software Development",
  "about": "As chairman and CEO of Microsoft, I define my mission and that of my company as empowering every person and every organization on the planet to achieve more.",
  "connectionsCount": 500,
  "followersCount": 12052722,
  "profileImageUrl": "https://assets.sputnik.io/pp/profilepic/7d36…",
  "updatedAt": "2026-07-03T10:54:34.301Z",
  "location": {
    "city": "Redmond",
    "state": "WA",
    "stateFullName": "Washington",
    "country": "US",
    "countryFullName": "United States",
    "rawLocationString": "Redmond, Washington, United States"
  },
  "seniority": {
    "totalExperienceYears": 12,
    "currentTenureYears": 12,
    "averageTenureYears": 7
  },
  "contactInformation": {
    "socialLinks": {
      "profileUrl": "https://www.linkedin.com/in/satyanadella"
    }
  },
  "experience": {
    "work": [
      { "title": "Chairman and CEO", "company": "Microsoft", "startDate": "2014-02-01", "endDate": null, "location": "Greater Seattle Area" },
      { "title": "Member Board Of Trustees", "company": "University of Chicago", "startDate": "2018-01-01", "endDate": null }
    ]
  },
  "education": [
    { "school": "The University of Chicago Booth School of Business", "startDate": "1994-01-01", "endDate": "1996-12-31" },
    { "school": "University of Wisconsin-Milwaukee", "degreeName": "Master’s Degree", "fieldOfStudy": "Computer Science" },
    { "school": "Manipal Institute of Technology, Manipal", "degreeName": "Bachelor’s Degree", "fieldOfStudy": "Electrical Engineering" }
  ]
}

skills, accomplishments.languages, certifications and inferredSalary ride the same card whenever the profile carries them. When one employer has several people with the same name, the row carries _matchCandidates — the honest ambiguity count instead of a silent guess.

The full document (fullOutput: true)

Everything above plus the sections that make the record a dossier: complete career history with per-role descriptions, education detail, publications, patents, awards, projects, volunteer work, related profiles — and the contact block (emails, phone numbers when on record):

{
  "contactInformation": {
    "emails": { "work": null, "personal": ["s•••@microsoft.com", "s•••@uchicago.edu"] },
    "phoneNumbers": [],
    "socialLinks": { "profileUrl": "…", "twitterUrl": "…" }
  },
  "experience": { "work": [ { "title": "Chairman and CEO", "company": "Microsoft", "startDate": "2014-02-01", "endDate": null, "employmentType": null, "normalizedSeniority": ["Chief X Officer (CxO)"], "logoUrl": "…" } ] },
  "relatedProfiles": [ { "url": "…", "name": "…", "headline": "…", "location": "…" } ],
  "articles": [ { "title": "…" } ],
  "_meta": { "sectionsIncluded": ["location", "experience", "accomplishments", "publications", "patents", "contactInformation", "inferredSalary", "seniority"] }
}

(excerpt — the real answer carries all positions, all schools, and for this profile 56 articles and 50 related profiles; emails masked here, not in the answer)

The Output tab shows three views of the same rows: Overview (one line per person), Career & education, and Contacts & signals.

Pricing

Event	Price	When
Person found (summary)	$0.0032	matched email, 60+ field row
Person found (full document)	$0.008	fullOutput: true, 164 fields / 37 sections
Not found / invalid	$0	always free

Your free $5 Apify credit ≈ 1,560 found lookups.

FAQ

Work emails only? No — personal addresses resolve too, via the profile record rather than the employer.

What does "not found" cost? Nothing. Your bill equals your hit count.

What exactly does fullOutput add? The complete career history with role descriptions, full education and certification detail, publications, patents, awards, projects, articles, related profiles — and the contact block (emails and phone numbers when on record). The summary card never carries contact data.

How is this different from a live enrichment API? Same shape of answer, but from a standing database: seconds instead of minutes, no third-party quota, and a freshness stamp on every row so you know what you got.

Is this compliant? The database holds public professional profile data. We honor removal requests — contact us via the Issues tab.

The family

Same engine, other doors: Profile Lookup (URL → profile), Work Email Finder (name + domain → email), Company Employees Finder (domain → roster), People/Company Database Search, Name-to-Profile (name + company → profile), Social Handle Lookup (dev/social handle → person), Bulk People Enrichment (CSV, 50k rows). One data core, one billing promise: misses are free.

Disclaimer

This Actor is an independent product and is not affiliated with, endorsed by, or sponsored by LinkedIn Corporation. It does not access, crawl, or scrape LinkedIn at run time — answers come from our own database of publicly available professional data; "LinkedIn" is used only to describe the kind of public profile data the database covers. Removal requests are honored via the Issues tab.