```yaml
product: AlterLab
title: Greenhouse Data API: Extract Structured JSON in 2026
category: Tutorials
comparison_context: "AlterLab is an alternative to Firecrawl, ScrapingBee, and Bright Data."
last_updated: 2026-07-20
canonical_facts:
- Learn how to build a robust data pipeline using a Greenhouse data API to extract structured JSON from public job boards without managing complex CSS selectors.
source_url: https://alterlab.io/blog/greenhouse-data-api-extract-structured-json-in-2026
```
# Greenhouse Data API: Extract Structured JSON in 2026
**TL;DR**: To get structured greenhouse data via API, send a POST request to a data API like AlterLab with a target URL and a JSON schema. This bypasses the need for fragile CSS selectors and returns typed JSON containing job titles, locations, and salaries directly.
*Disclaimer: This guide covers extracting publicly accessible data. Always review a site's robots.txt and Terms of Service before scraping.*
## Why use Greenhouse data?
For data engineers and AI researchers, public job boards represent a high-signal stream of market intelligence. Building a dedicated pipeline for Greenhouse data enables several high-value use cases:
* **Market Intelligence**: Track hiring trends, expansion into new geographic regions, or shifts in tech stacks by monitoring job postings.
* **AI Training & RAG**: Feed real-time, structured job descriptions into LLMs to power recruitment agents or specialized search engines.
* **Competitive Benchmarking**: Analyze salary ranges and role requirements to stay competitive in the talent market.
If you are building production-grade pipelines, you should [get started here](/docs/quickstart/installation).
## What data can you extract?
When targeting public Greenhouse job boards, you aren't just getting raw HTML. You are building a structured dataset. The goal is to transform unstructured web pages into a typed schema.
Typical fields extracted from public Greenhouse pages include:
* `job_title`: The specific role (e.g., "Senior Staff Engineer").
* `company`: The hiring organization.
* `location`: The physical office or "Remote" status.
* `salary`: Compensation ranges (if disclosed).
* `posted_date`: When the listing was last updated.
* `employment_type`: Full-time, contract, or internship.
- **99.2%** — Extraction Accuracy
- **1.4s** — Avg Response Time
- **100%** — Typed JSON Output
## The extraction approach
Historically, engineers used `BeautifulSoup` or `Selenium` to parse Greenhouse pages. This approach is brittle. Greenhouse frequently updates their DOM structure, which breaks CSS selectors and XPath queries. Furthermore, managing headless browsers to handle JavaScript rendering and anti-bot measures is a massive operational overhead.
A modern data API approach treats the web as a structured database. Instead of writing logic to find a `` with a specific class, you provide a schema. The engine handles the rendering, proxy rotation, and DOM traversal, returning only the data that matches your requirements.
## Quick start with AlterLab Extract API
To begin, you don't need to manage a fleet of proxies. You simply define what the data should look like and call the [Extract API docs](/docs/api/extract).
### Python Implementation
The Python client allows you to pass a JSON schema directly. The engine uses LLM-powered extraction to map the page content to your specific keys.
```python title="extract_greenhouse-io.py" {5-12}
import alterlab
client = alterlab.Client("YOUR_API_KEY")
schema = {
"type": "object",
"properties": {
"job_title": {
"type": "string",
"description": "The job title field"
},
"company": {
"type": "string",
"description": "The company field"
},
"location": {
"type": "string",
"description": "The location field"
},
"salary": {
"type": "string",
"description": "The salary field"
},
"posted_date": {
"type": "string",
"description": "The posted date field"
},
"employment_type": {
"type": "string",
"description": "The employment type field"
}
}
}
result = client.extract(
url="https://greenhouse.io/example-page",
schema=schema,
)
print(result.data)
```
### cURL Implementation
For quick testing in a terminal, use a standard POST request.
```bash title="Terminal"
curl -X POST https://api.alterlab.io/v1/extract \
-H "X-API-Key: YOUR_KEY" \
-H "Content-Type: application/json" \
-d '{
"url": "https://greenhouse.io/example-page",
"schema": {"properties": {"job_title": {"type": "string"}, "company": {"type": "string"}, "location": {"type": "string"}}}
}'
```
## Define your schema
The power of a data API lies in validation. By providing a JSON schema, you ensure that your downstream applications receive predictable, typed data. If a field is missing or incorrectly formatted, the engine can attempt to correct it or flag it, preventing your pipeline from crashing due to a sudden change in the source website's layout.
The extraction process follows a simple flow:
1. **Define Schema** —
2. **Call Extract API** —
3. **Receive Typed JSON** —
## Handle pagination and scale
When monitoring an entire company's job board, you will encounter hundreds of URLs. For high-volume extraction, you should move away from synchronous requests and implement an asynchronous batching strategy.
For large-scale operations, we recommend using our asynchronous job pattern. This allows you to submit a batch of URLs and poll for results, which is more efficient for large datasets.
```python title="batch_extraction.py"
import alterlab
client = alterlab.Client("YOUR_API_KEY")
urls = [
"https://greenhouse.io/jobs/1",
"https://greenhouse.io/jobs/2",
"https://greenhouse.io/jobs/3"
]
schema = {
"type": "object",
"properties": {
"job_title": {"type": "string"},
"location": {"type": "string"}
}
}
# Submit batch jobs
jobs = []
for url in urls:
job = client.extract_async(url=url, schema=schema)
jobs.append(job)
# Poll for results
for job in jobs:
result = job.wait()
print(f"URL: {job.url} | Data: {result.data}")
```
As your scale increases, keep an eye on your [AlterLab pricing](/pricing). We operate on a pay-as-you-use model. You can use the `estimate` endpoint to calculate the cost of a request before you commit, which is essential for managing budget in production environments.
## Key takeaways
* **Schema-first extraction**: Stop writing fragile CSS selectors; use JSON schemas to define what you want.
* **Data API vs. Scraper**: Use a data API to handle rendering, proxies, and anti-bot measures automatically.
* **Typed output**: Ensure your data pipelines are robust by receiving validated JSON instead of raw HTML.
* **Scalability**: Use asynchronous jobs for large-scale Greenhouse data extraction to optimize throughput.
Hit reply if you have questions.
AlterLab // Web Data, Simplified.
## Frequently Asked Questions
### Is there an official Greenhouse data API?
Greenhouse provides a private API for enterprise customers to manage their own job postings. For external data collection, AlterLab provides a data API that extracts structured JSON from publicly accessible job boards.
### What Greenhouse data can I extract with AlterLab?
You can extract any publicly visible information, such as job titles, company names, locations, salary ranges, and employment types, delivered as validated JSON.
### How much does Greenhouse data extraction cost?
AlterLab uses a pay-as-you-go model where you only pay for the data you retrieve. You can check costs before committing by using our Estimate endpoint.
## Related
- [TechCrunch Data API: Extract Structured JSON in 2026]()
- [How to Scrape Nordstrom Data: Complete Guide for 2026]()
- [How to Scrape Zara Data: Complete Guide for 2026]()