Glassdoor contains valuable data -- employer_id and profile, overall and category ratings, reviews (max 3 per response), salary bands by title, and more. Scraping this data directly means dealing with anti-bot detection, CAPTCHAs, IP rotation, and constantly breaking selectors. The Scavio API handles all of that and returns clean, structured JSON from a single POST request.
This tutorial shows you how to scrape Glassdoor using Python and the Scavio API. By the end, you will have a working Python script that fetches real-time Glassdoor data and parses the results.
Prerequisites
- Python installed on your machine
- A Scavio API key (free tier includes 50 credits on signup -- no credit card required)
Step 1: Install Dependencies
Install requests to make HTTP requests:
pip install requestsStep 2: Make Your First Glassdoor Search
Send a POST request to the Scavio Glassdoor API endpoint with your query. The API returns structured JSON with employer_id and profile, overall and category ratings, reviews (max 3 per response), and more.
# This page has no dedicated endpoint yet, so the sample runs a Google web search.
import requests
API_KEY = "sk_live_your_key"
query = "Stripe"
response = requests.post(
"https://api.scavio.dev/api/v2/google",
headers={
"Authorization": f"Bearer {API_KEY}",
"Content-Type": "application/json",
},
json={"query": query},
timeout=60,
)
response.raise_for_status()
data = response.json()
rows = data.get("organic_results") or []
for row in rows[:5]:
print(row.get("title"))
print(" ", row.get("link"), row.get("snippet"))Step 3: Example Response
The API returns structured JSON. Here is an example response for a Glassdoor search:
{
"search_parameters": { "q": "cold brew coffee", "hl": "en", "gl": "us" },
"organic_results": [
{
"position": 1,
"title": "how do you guys make cold brew? : r/Coffee",
"link": "https://www.reddit.com/r/Coffee/comments/oi7rm7/how_do_you_guys_make_cold_brew/",
"snippet": "i wanna learn how to make cold brew coffee but theres a lot of ways...",
"source": "Reddit"
}
],
"related_searches": [{ "query": "cold brew ratio", "link": "https://www.google.com/search?q=cold+brew+ratio" }],
"response_time": 2841,
"credits_used": 1,
"credits_remaining": 4821
}Every field is structured and typed -- no HTML parsing, no CSS selectors, no regex extraction. Your Python code can access any field directly.
Step 4: Full Working Example
Here is a complete, runnable Python script that searches Glassdoor and prints the results:
"""
Search Glassdoor data with the Scavio API.
POST /api/v2/google - rows come back under organic_results, 1 credit per call.
"""
import json
import os
import requests
# This page has no dedicated endpoint yet, so the sample runs a Google web search.
API_URL = "https://api.scavio.dev/api/v2/google"
API_KEY = os.environ["SCAVIO_API_KEY"]
def search_glassdoor(query: str) -> dict:
response = requests.post(
API_URL,
headers={
"Authorization": f"Bearer {API_KEY}",
"Content-Type": "application/json",
},
json={"query": query},
timeout=60,
)
response.raise_for_status()
return response.json()
if __name__ == "__main__":
data = search_glassdoor("Stripe")
print(json.dumps(data, indent=2))
rows = data.get("organic_results") or []
for row in rows[:5]:
print(row.get("title"))
print(" ", row.get("link"), row.get("snippet"))Why Use Scavio Instead of Scraping Glassdoor Directly?
- No proxy management. Direct scraping requires rotating proxies to avoid IP bans. Scavio handles all of this server-side.
- No CAPTCHA solving. Glassdoor aggressively blocks automated requests. Scavio returns clean data every time.
- Structured JSON output. No HTML parsing or CSS selector maintenance. Get typed, consistent data from every request.
- Multi-platform in one API. Search Google, Amazon, YouTube, and Walmart from the same API key with the same authentication pattern.
- Free tier included. 50 credits on signup with no credit card required. Each search costs 1 credit.