Hiring Signals → Notion — Track Who's Hiring & Which Teams Are Growing

Feed it a list of companies. Get back a hiring-signal scorecard for each — and (optionally) a ready-to-use Notion database, built in the same run.

Hiring is the most honest signal a company sends. A wave of new engineering reqs means a product push. A sudden run of sales and BD roles means a go-to-market expansion. New offices in a new city means geographic growth. This Actor reads a company's live, public job board and turns it into a one-line read on where that company is putting its money — then drops it straight into your Notion workspace if you want it there.

Built for recruiters, sales/BD teams, competitive-intel analysts, and investors who want to know who's growing before it's obvious.

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What you get per company

Each scorecard contains:

Field	What it tells you
company / company_slug	The company and its resolved Greenhouse board
open_roles_count	Total live open roles right now
departments	Every hiring team with an open-role count (ranked)
notable_roles	A handful of representative open titles
locations	Where they're hiring, top locations ranked
hiring_velocity_note	Plain English — e.g. "expanding eng (28) + sales (11) — 64 open roles total"
source	Direct link to the live job board
data_source	Always the public Greenhouse job-board API
as_of_timestamp	When the snapshot was taken (UTC)

📊 Sample Output

Real scorecards returned by a live run across 8 companies — every value below comes straight from the public Greenhouse job-board API at run time:

Company	Open roles	Top hiring department	Hiring-velocity note
Stripe	506	1150 Solutions Architecture (23)	expanding eng (181) + ops (108) + sales (59) — 506 open roles total
Anthropic	378	Sales (74)	expanding eng (220) + sales (68) + ops (38) — 378 open roles total
Airbnb	222	Software Engineering (67)	expanding eng (87) + sales (44) + ops (38) — 222 open roles total
Figma	171	Sales (64)	expanding sales (75) + eng (48) + ops (18) — 171 open roles total
Reddit	146	Large Customer Sales (26)	expanding eng (82) + sales (36) + product (4) — 146 open roles total
GitLab	138	EMEA - Commercial (14)	expanding eng (49) + sales (42) + ops (18) — 138 open roles total
Asana	133	Product Engineering (30)	expanding eng (62) + sales (25) + ops (16) — 133 open roles total
Discord	70	Core Tech Engineering (14)	expanding eng (41) + ops (10) + product (7) — 70 open roles total

Each dataset row also carries the full ranked departments and locations arrays, notable_roles, the live source link, data_source, and as_of_timestamp — see the full JSON object under Example output below.

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Optional: one-run delivery into Notion

Add a Notion MCP connector and the Actor builds a fresh Notion database for you in the same run — one row per company, columns for open roles, top departments, notable roles, top locations, hiring-velocity note, source, and snapshot date. The Actor never sees your Notion credentials; Apify injects them server-side through the connector. Leave the connector blank and you simply get the dataset.

To use it: authorize a Notion MCP connector in Apify Console → Settings → API & Integrations, then pick it in the notionConnector field. Optionally pass a notionParentId to nest the database under a specific page.

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Input

{
  "companies": ["stripe", "airbnb", "anthropic"],
  "notionConnector": null,
  "notionParentId": null
}

• companies — slugs or company names. Names are normalized to Greenhouse slug candidates automatically (e.g. "Air BnB" → airbnb). Verified-working slugs: stripe, airbnb, anthropic, figma, discord, gitlab, reddit, asana. Companies on a different ATS (Lever, Ashby, Workday) are returned with a clear "not found on Greenhouse" note rather than failing.
• notionConnector — optional Notion MCP connector. Blank = dataset-only.
• notionParentId — optional Notion page ID to create the database under.

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Example output

{
  "company": "stripe",
  "company_slug": "stripe",
  "open_roles_count": 312,
  "departments": [
    { "department": "Engineering", "open_roles": 118 },
    { "department": "Sales", "open_roles": 41 },
    { "department": "Operations", "open_roles": 22 }
  ],
  "notable_roles": ["Staff Software Engineer", "Account Executive, Mid-Market", "Product Manager, Payments"],
  "locations": [
    { "location": "Remote", "open_roles": 96 },
    { "location": "San Francisco", "open_roles": 44 }
  ],
  "hiring_velocity_note": "expanding eng (118) + sales (41) + ops (22) — 312 open roles total",
  "source": "https://boards.greenhouse.io/stripe",
  "data_source": "greenhouse.io (public job-board API)",
  "as_of_timestamp": "2026-06-13T16:40:00+00:00"
}

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How it works

The Actor queries the open, unauthenticated Greenhouse public job-board API (boards-api.greenhouse.io) — the same data that renders on boards.greenhouse.io/<company>. No logins, no scraping behind walls, public data only. It then aggregates departments, locations, and role titles into a single scorecard and phrases a hiring-velocity summary.

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Use cases

• Recruiters — spot which competitors are mass-hiring for the exact roles you place.
• Sales / BD — a company opening 40 sales roles is a company about to buy sales tooling. Time your outreach.
• Competitive intel — track a rival's headcount investment by team, week over week.
• Investors — hiring velocity is a leading indicator of growth that shows up before revenue does.

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🔗 Related NexGenData actors

• Greenhouse Jobs Scraper — the raw role-level data behind these scorecards: title, location, departments, full description HTML.
• LinkedIn Jobs Scraper — hiring data from LinkedIn for companies not on Greenhouse.
• Hiring Signal Detector — deeper trend detection across hiring sources.
• 🏢 About NexGenData — our full fleet of business-data and Notion-delivery actors.

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Public data only. Use responsibly and follow applicable employment, outreach, and privacy laws.