6.140 Ex-Big-Tech-Gründer und C-Level-Profile per Datenbank-Abfrage identifizieren

Part of the series "GTM workflows with Apify and Claude". The series opener explains the stack and the four ways to run everything below.

Some lists are wanted by half a dozen unrelated readers at once. People who left a large technology company recently and are now building something is one of them. A seed investor wants them before the round is announced. A startup bank or a corporate-card company wants them the week they incorporate. Cloud providers and developer-tool vendors run startup programs for exactly this person. Incorporation, accounting and immigration services want them in month one. Recruiters want them because the first engineering hires happen before a job ad exists. And if you are an engineer looking for a founding role, you want the same list for the same reason.

What these readers have in common is that they usually get this list late, from announcements. Here is the list built from career data on 2026-10-10, in full, with the LinkedIn People Search Actor on Apify. I use it because "used to work at these ten companies, left after this year, now holds this seniority" is three input fields, and the count comes back before any row does, so the whole segment can be pulled instead of a sample.

{ "mode": "count", "countries": [ "us", "gb", "de", "nl", "ca" ], "pastEmployerDomains": [ "google.com", "meta.com", "amazon.com", "microsoft.com", "apple.com", "salesforce.com", "oracle.com", "cisco.com", "ibm.com", "intel.com" ], "leftPastEmployerAfterYear": 2025, "seniority": [ "founder", "cxo" ] } Enter fullscreen mode Exit fullscreen mode 6,140 people. The Actor adds one thing on its own, visible in the _input echo on the result: people still employed at the ten companies are excluded, so an "Amazon, then Amazon again" promotion does not count as leaving.

For context I ran the wider population as segments in the same call family, which returns counts only: everyone who left those ten companies in 2025 or later and lives in the five countries is 115,137 people. Of those, 26,583 now work at companies of 500 people or fewer, 8,796 of them as managers or above, and 30,840 show no current position at all. The founder and C-level cut is the 6,140 this article is about.

Ten more variants, counts only, to understand what is inside before pulling rows:

The lesson from the table is that the raw definition is already clean: dropping students and first-jobbers removes 3%, not 30%. I pulled the raw definition and did the cuts on the rows.

mode: "market", 168 aggregate rows, no person rows:

Three "stealth" spellings at the top of the employer column is the whole thesis of the list in one line.

mode: "people", profileDetail: "row", the same filters. One practical detail: Apify caps a single run at the account's remaining monthly limit, so the list came in two runs, the second excluding the first:

Run 1, maxResults: 4500: 11 minutes 32 seconds, 4,500 rows. Run 2, the same input with "excludeDatasets": ["<dataset id of run 1>"] and maxResults: 2500: 6 minutes 19 seconds, 1,491 rows, and a note row saying 149 people were already delivered or repeated and were skipped.

Total: 5,991 distinct people. The count said 6,140 because it includes the historical snapshots the Actor later recognised as repeats and did not deliver twice.

The cut that matters for most of the readers above is "started the current role in 2025 or later, at a company of ten people or fewer, or with no size on file": 2,824 people. That is the pre-seed universe for these ten alumni networks in five countries, as of this week.

To see careers, not just titles, I pulled 300 people from the tightest cut at the profile tier with compact: true, which returns the five latest positions with dates, education and top skills in about 2 KB per person instead of the full record:

{ "mode": "people", "profileDetail": "profile", "compact": true, "countries": [ "us" ], "pastEmployerDomains": [ "google.com", "meta.com", "amazon.com", "microsoft.com", "apple.com", "salesforce.com", "oracle.com", "cisco.com", "ibm.com", "intel.com" ], "leftPastEmployerAfterYear": 2025, "seniority": [ "founder", "cxo" ], "currentRoleStartedAfterYear": 2025, "employeeCountMax": 50, "includeUnknownEmployerSize": true, "excludeTitleKeywords": [ "advisor", "board member", "investor", "member" ], "maxResults": 300 } Enter fullscreen mode Exit fullscreen mode Run log: 3 minutes 27 seconds, 300 people, 294 with the full profile record and 4 with only the row-level record on file.

From the positions: of the 223 Big Tech exits visible in the five-position window, 58 were from Amazon, 54 Microsoft, 31 Google, 28 Meta, 20 Apple, 14 Salesforce, 10 Oracle, 6 IBM, 2 Intel. Exits are spread across every month of 2025 with bumps in June–July and December, and continue into the first quarter of 2026. Median tenure at the Big Tech position was two years, so these are mostly people who did a stint, not a career. Exactly 2 of the 300 Big Tech positions were internships. Headlines included two YC W26 companies, several "building in stealth", and a long tail of CPAs, consultants and advisors who use the founder title for a practice. Education was on file for 276 of 300.

My reflex from the sales lists was to pass the shortlist to the email Actor. I sent 60 profile URLs to LinkedIn Email Finder: 57 came back with an address, and 54 of those were at a previous employer, often the Big Tech one the person had just left. Only 3 were at the current company. Of course: a two-month-old stealth startup has no email format on record.

For founders the right key is the personal mailbox, and the search has a tier for it. Same shortlist filter, profileDetail: "contacts", mustHave: ["personalEmail"]:

Run log: 48 seconds, 150 people delivered, 72 skipped for having no personal email. Of the 150: personal email 149, work email 139 (8 at the current company), direct phone 67, social links 92 (24 GitHub, 77 X).

That is the founder shortlist a seed fund or a startup bank would want: name, what they left and when, what they are building, a personal email, often a phone, and for a quarter of them a GitHub profile.

Everything above is one combination of pastEmployerDomains, leftPastEmployerAfterYear and seniority. Other combinations I would run next, each a count away:

In Claude Code with the Apify MCP server (setup in the series opener):

"Use b2bsearch/people-database-search. Past employers google.com, meta.com, amazon.com, microsoft.com, apple.com, salesforce.com, oracle.com, cisco.com, ibm.com, intel.com; left in 2025 or later; seniority founder or cxo; countries us, gb, de, nl, ca. Run mode count and mode market. Then one segments call comparing cuts by career start year, current role start, company size and excluded titles. Summarise and tell me what the top employers in the market view say about the segment. Do not run mode people without my confirmation; when I confirm, respect the maxResults I give and report rows delivered and rows skipped."

The agent noticed the "stealth" employers and the 44% unknown industry on its own and explained both correctly: new companies with no record yet. Then it waited.

Actor b2bsearch/people-database-search. Alumni signal: pastEmployerDomains (site domains; brand domains resolve to the employer record) with leftPastEmployerAfterYear; the Actor excludes people still at those domains. Founders and executives: seniority: ["founder", "cxo"].

Tighten with currentRoleStartedAfterYear, employeeCountMax plus includeUnknownEmployerSize: true (new companies have no headcount on file), careerStartYearMax to drop interns and first-jobbers, excludeTitleKeywords for advisor and board titles. A single run is capped at the account's remaining monthly limit; split large pulls and pass earlier dataset ids in excludeDatasets. For founders use profileDetail: "contacts" with mustHave: ["personalEmail"] rather than a work-email finder; new companies have no work email format on record.

Company card fields on each row: domain, employeeCount, industry, founded, funding. Per-tier pricing is on the Actor page; run mode: "count" and report it before any people run.

Next in the series: every sales leader who changed jobs in the last twelve months, in one query, refreshed weekly.