# BambooHR Jobs Scraper (`oguzcankaraman/bamboohr-jobs-scraper`) Actor
Extract every job posting from any company's BambooHR career site ({company}.bamboohr.com/careers). Clean normalized output with full descriptions, compensation, locations, and apply URLs. Supports multiple companies per run.
- **URL**: https://apify.com/oguzcankaraman/bamboohr-jobs-scraper.md
- **Developed by:** [Oğuzcan Karaman](https://apify.com/oguzcankaraman) (community)
- **Categories:** Automation, Jobs
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 1 bookmarks
- **User rating**: No ratings yet
## Pricing
from $2.00 / 1,000 results
This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event
## What's an Apify Actor?
Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".
## How to integrate an Actor?
If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.
In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md):
```bash
npm install apify-client
```
In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md):
```bash
pip install apify-client
```
In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md):
````bash
# MacOS / Linux
curl -fsSL https://apify.com/install-cli.sh | bash
# Windows
irm https://apify.com/install-cli.ps1 | iex
```bash
In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md).
If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md).
For usage examples, see the [API](#api) section below.
For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).
# README
## BambooHR Jobs Scraper
Extract **every job posting from any company that hires through BambooHR** — with clean, normalized, analysis-ready output. No API keys, no setup: paste a company's career-site URL (like `lucid.bamboohr.com/careers`) or its subdomain and run.
Tens of thousands of small and mid-size companies publish their jobs on BambooHR career sites. This Actor turns those pages into structured data using the same public endpoints the pages themselves use.
### What you get
Every job is returned in a **normalized schema** shared by all my ATS scrapers (SmartRecruiters, Workable, Recruitee, …), so you can combine sources without any mapping work:
- ✅ **Full job description** (plain text *and* HTML)
- ✅ **Compensation parsing** — BambooHR employers often fill a dedicated compensation field (e.g. `$130,000`); it's parsed into structured min/max/currency, with a fallback to salary mentions in the description
- ✅ **Structured location** — city, state, country, and a `remote` flag
- ✅ Department, employment type, minimum experience, posted date
- ✅ Direct **posting URL and apply URL** for every job
- ✅ **Multiple companies in one run**, automatic deduplication
### Use cases
- **Job boards & aggregators** — SMB jobs that big aggregators often miss
- **Sales lead generation** — SMBs hiring for role X are buying tools for X
- **Recruiting & talent intelligence** — track hiring at thousands of smaller companies
- **Salary benchmarking** — structured compensation data straight from job ads
- **AI agents & LLM pipelines** — clean JSON for RAG or agent tools
### Input
```json
{
"companies": ["https://lucid.bamboohr.com/careers", "neighbor"],
"keyword": "engineer",
"location": "remote",
"postedAfter": "2026-06-01",
"includeFullDescription": true,
"maxJobsPerCompany": 500
}
````
### Output example
```json
{
"id": "15",
"source": "bamboohr",
"title": "IT Security Engineer",
"company": { "identifier": "lucid", "name": null },
"location": { "city": "Mayfair", "region": "London, City of", "country": null, "remote": false },
"department": "IT",
"employmentType": "Full-Time",
"experienceLevel": "Manager/Supervisor",
"salary": { "min": 130000, "max": null, "currency": "USD", "period": null, "raw": "$130,000" },
"postedAt": "2026-01-15",
"postingUrl": "https://lucid.bamboohr.com/careers/15",
"descriptionText": "...",
"descriptionHtml": "..."
}
```
Download results as **JSON, CSV, Excel, or XML**, or fetch them via the [Apify API](https://docs.apify.com/api/v2), scheduled runs, and webhooks.
### How to find a company's subdomain
Open the company's careers page. If the URL looks like `https://lucid.bamboohr.com/careers`, the subdomain is `lucid`. You can paste the full URL as well — the Actor extracts it automatically.
### FAQ
**Is this legal?** The Actor only collects publicly available job postings that companies intentionally publish to attract applicants. No personal data is collected.
**A company returns "not found" — why?** The subdomain is wrong, or the company doesn't use BambooHR's public careers page.
**Need SmartRecruiters, Workable, Recruitee, or another ATS?** Check my profile — all my ATS scrapers share the exact same output schema.
# Actor input Schema
## `companies` (type: `array`):
BambooHR subdomains (e.g. lucid) or career-site URLs (e.g. https://lucid.bamboohr.com/careers). One entry per company.
## `keyword` (type: `string`):
Optional case-insensitive filter on the job title, e.g. engineer.
## `location` (type: `string`):
Optional case-insensitive filter matched against city, state, and country of each posting, e.g. London, remote.
## `postedAfter` (type: `string`):
Only include jobs posted on or after this date (YYYY-MM-DD). Requires detail fetches, so it works even when full descriptions are disabled.
## `includeFullDescription` (type: `boolean`):
Fetch the full job ad (description, compensation, posted date) for every posting. Enables salary parsing. Slightly slower because it makes one extra request per job.
## `maxJobsPerCompany` (type: `integer`):
Stop after this many jobs per company. Use 0 for unlimited.
## Actor input object example
```json
{
"companies": [
"lucid"
],
"includeFullDescription": true,
"maxJobsPerCompany": 0
}
```
# Actor output Schema
## `jobs` (type: `string`):
All scraped job postings in the normalized schema: title, company, location, department, employment type, parsed salary, posting/apply URLs, and full description.
## `jobsCsv` (type: `string`):
The same dataset as CSV, ready for Excel or Google Sheets.
## `run` (type: `string`):
Public link to this run with logs and statistics.
# API
You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.
## JavaScript example
```javascript
import { ApifyClient } from 'apify-client';
// Initialize the ApifyClient with your Apify API token
// Replace the '' with your token
const client = new ApifyClient({
token: '',
});
// Prepare Actor input
const input = {
"companies": [
"lucid"
]
};
// Run the Actor and wait for it to finish
const run = await client.actor("oguzcankaraman/bamboohr-jobs-scraper").call(input);
// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
console.dir(item);
});
// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs
```
## Python example
```python
from apify_client import ApifyClient
# Initialize the ApifyClient with your Apify API token
# Replace '' with your token.
client = ApifyClient("")
# Prepare the Actor input
run_input = { "companies": ["lucid"] }
# Run the Actor and wait for it to finish
run = client.actor("oguzcankaraman/bamboohr-jobs-scraper").call(run_input=run_input)
# Fetch and print Actor results from the run's dataset (if there are any)
print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"])
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
print(item)
# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start
```
## CLI example
```bash
echo '{
"companies": [
"lucid"
]
}' |
apify call oguzcankaraman/bamboohr-jobs-scraper --silent --output-dataset
```
## MCP server setup
```json
{
"mcpServers": {
"apify": {
"command": "npx",
"args": [
"mcp-remote",
"https://mcp.apify.com/?tools=oguzcankaraman/bamboohr-jobs-scraper",
"--header",
"Authorization: Bearer "
]
}
}
}
```
## OpenAPI specification
```json
{
"openapi": "3.0.1",
"info": {
"title": "BambooHR Jobs Scraper",
"description": "Extract every job posting from any company's BambooHR career site ({company}.bamboohr.com/careers). Clean normalized output with full descriptions, compensation, locations, and apply URLs. Supports multiple companies per run.",
"version": "0.1",
"x-build-id": "WsRlAKFCocZ4RmDiS"
},
"servers": [
{
"url": "https://api.apify.com/v2"
}
],
"paths": {
"/acts/oguzcankaraman~bamboohr-jobs-scraper/run-sync-get-dataset-items": {
"post": {
"operationId": "run-sync-get-dataset-items-oguzcankaraman-bamboohr-jobs-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
},
"/acts/oguzcankaraman~bamboohr-jobs-scraper/runs": {
"post": {
"operationId": "runs-sync-oguzcankaraman-bamboohr-jobs-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor and returns information about the initiated run in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/runsResponseSchema"
}
}
}
}
}
}
},
"/acts/oguzcankaraman~bamboohr-jobs-scraper/run-sync": {
"post": {
"operationId": "run-sync-oguzcankaraman-bamboohr-jobs-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
}
},
"components": {
"schemas": {
"inputSchema": {
"type": "object",
"required": [
"companies"
],
"properties": {
"companies": {
"title": "Companies",
"type": "array",
"description": "BambooHR subdomains (e.g. lucid) or career-site URLs (e.g. https://lucid.bamboohr.com/careers). One entry per company.",
"items": {
"type": "string"
}
},
"keyword": {
"title": "Keyword",
"type": "string",
"description": "Optional case-insensitive filter on the job title, e.g. engineer."
},
"location": {
"title": "Location filter",
"type": "string",
"description": "Optional case-insensitive filter matched against city, state, and country of each posting, e.g. London, remote."
},
"postedAfter": {
"title": "Posted after",
"pattern": "^\\d{4}-\\d{2}-\\d{2}$",
"type": "string",
"description": "Only include jobs posted on or after this date (YYYY-MM-DD). Requires detail fetches, so it works even when full descriptions are disabled."
},
"includeFullDescription": {
"title": "Include full description",
"type": "boolean",
"description": "Fetch the full job ad (description, compensation, posted date) for every posting. Enables salary parsing. Slightly slower because it makes one extra request per job.",
"default": true
},
"maxJobsPerCompany": {
"title": "Max jobs per company",
"minimum": 0,
"type": "integer",
"description": "Stop after this many jobs per company. Use 0 for unlimited.",
"default": 0
}
}
},
"runsResponseSchema": {
"type": "object",
"properties": {
"data": {
"type": "object",
"properties": {
"id": {
"type": "string"
},
"actId": {
"type": "string"
},
"userId": {
"type": "string"
},
"startedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"finishedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"status": {
"type": "string",
"example": "READY"
},
"meta": {
"type": "object",
"properties": {
"origin": {
"type": "string",
"example": "API"
},
"userAgent": {
"type": "string"
}
}
},
"stats": {
"type": "object",
"properties": {
"inputBodyLen": {
"type": "integer",
"example": 2000
},
"rebootCount": {
"type": "integer",
"example": 0
},
"restartCount": {
"type": "integer",
"example": 0
},
"resurrectCount": {
"type": "integer",
"example": 0
},
"computeUnits": {
"type": "integer",
"example": 0
}
}
},
"options": {
"type": "object",
"properties": {
"build": {
"type": "string",
"example": "latest"
},
"timeoutSecs": {
"type": "integer",
"example": 300
},
"memoryMbytes": {
"type": "integer",
"example": 1024
},
"diskMbytes": {
"type": "integer",
"example": 2048
}
}
},
"buildId": {
"type": "string"
},
"defaultKeyValueStoreId": {
"type": "string"
},
"defaultDatasetId": {
"type": "string"
},
"defaultRequestQueueId": {
"type": "string"
},
"buildNumber": {
"type": "string",
"example": "1.0.0"
},
"containerUrl": {
"type": "string"
},
"usage": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "integer",
"example": 1
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
},
"usageTotalUsd": {
"type": "number",
"example": 0.00005
},
"usageUsd": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "number",
"example": 0.00005
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
}
}
}
}
}
}
}
}
```