This guide covers extracting publicly accessible data. Always review a site's robots.txt and Terms of Service before scraping.

TL;DR

Scrape Glassdoor Interviews public pages using AlterLab's API with automatic anti-bot handling. Use Python or Node.js SDKs to extract interview data, then structure it with Cortex for typed JSON. Start at T1 tier and let AlterLab auto-escalate as needed.

Why collect jobs data from Glassdoor Interviews?

Glassdoor Interviews provides candid insights into hiring processes across industries. Engineers use this data for:

• Market research: Benchmark interview difficulty and question patterns against competitors
• Talent acquisition: Refine your interview process by analyzing successful candidate experiences
• Data science: Train models to predict role fit based on historical interview trends

Technical challenges

Glassdoor Interviews implements standard anti-bot protections: IP-based rate limiting, User-Agent validation, and lightweight JavaScript challenges on certain pages. Raw HTTP requests (T1/T2 tiers) often return CAPTCHAs or empty responses. AlterLab's Smart Rendering API automatically rotates residential proxies, adjusts headers, and escalates to browser rendering (T4) when needed—handling these challenges transparently.

Quick start with AlterLab API

First, install the SDK and get your API key from AlterLab dashboard. The API handles authentication, proxy rotation, and tier escalation.

Python example

import alterlab

client = alterlab.Client("YOUR_API_KEY")
response = client.scrape("https://glassdoor.com/Interview/google-software-engineer-interview-questions-SRCH_KO0,14.htm")
print(response.text[:500])  # Preview first 500 chars

Node.js example

import { AlterLab } from "alterlab";

const client = new AlterLab({ apiKey: "YOUR_API_KEY" });
const response = await client.scrape("https://glassdoor.com/Interview/google-software-engineer-interview-questions-SRCH_KO0,14.htm");
console.log(response.text.slice(0, 500));

cURL example

curl -X POST https://api.alterlab.io/v1/scrape \
  -H "X-API-Key: YOUR_KEY" \
  -d '{"url": "https://glassdoor.com/Interview/google-software-engineer-interview-questions-SRCH_KO0,14.htm"}'

Extracting structured data

Glassdoor interview pages follow consistent structures. Key selectors for public data:

• Job title: [data-test="job-title"]
• Company: [data-test="employer-name"]
• Interview difficulty: [data-test="difficulty-rating"]
• Questions: [data-test="question-text"]

For reliable extraction, combine these with AlterLab's built-in retry logic:

import alterlab
from parsel import Selector

client = alterlab.Client("YOUR_API_KEY")
html = client.scrape("https://glassdoor.com/Interview/meta-data-scientist-interview-questions-SRCH_KO0,15.htm").text
selector = Selector(text=html)

data = {
    "title": selector.css('[data-test="job-title"]::text').get(),
    "company": selector.css('[data-test="employer-name"]::text').get(),
    "difficulty": selector.css('[data-test="difficulty-rating"]::text').get(),
    "questions": [q.strip() for q in selector.css('[data-test="question-text"]::text').getall() if q.strip()]
}
print(data)

Structured JSON extraction with Cortex

AlterLab's Cortex API converts raw HTML to typed JSON using AI-driven schema matching—no CSS selectors needed. Define your expected output structure:

import alterlab

client = alterlab.Client("YOUR_API_KEY")
result = client.extract(
    url="https://glassdoor.com/Interview/amazon-product-manager-interview-questions-SRCH_KO0,16.htm",
    schema={
        "type": "object",
        "properties": {
            "job_title": {"type": "string"},
            "company": {"type": "string"},
            "location": {"type": "string"},
            "interview_difficulty": {"type": "string", "enum": ["Easy", "Medium", "Hard"]},
            "questions": {
                "type": "array",
                "items": {"type": "string"}
            },
            "date_posted": {"type": "string", "format": "date"}
        },
        "required": ["job_title", "company", "questions"]
    }
)
print(result.data)  # Returns validated JSON matching schema

Cost breakdown

AlterLab's pricing scales with resource usage. For Glassdoor Interviews (standard anti-bot protections), most requests succeed at T3 ($0.002/request) after automatic escalation from T1/T2. You only pay for the tier that succeeds.

View full pricing details → AlterLab auto-escalates tiers—start at T1 and pay only for the successful tier.

Best practices

• Rate limiting: Start with 1 request/second; increase gradually while monitoring HTTP 429 responses
• Robots.txt: Check https://glassdoor.com/robots.txt for crawl-delay directives (typically 5-10 seconds)
• Dynamic content: AlterLab's browser tiers (T4/T5) execute JavaScript and wait for network idle—no manual waits needed
• Error handling: Implement exponential backoff for 5xx responses; AlterLab returns tier-specific error codes
• Data freshness: Use the cache_bypass=true parameter for time-sensitive interview data

Scaling up

For production pipelines:

1. Batch requests: Use AlterLab's /batch endpoint to process 100 URLs in a single API call
2. Scheduling: Automate recurring scrapes with cron-like syntax via the Jobs API
3. Storage: Stream results directly to data warehouses (Snowflake, BigQuery) using webhooks