Glassdoor Reviews Scraper — Employee Reviews & Ratings in Clean JSON

Get employee review text and overall ratings from any Glassdoor company page — without an account, with three CSS/HTML fallback parsers so the actor keeps working when Glassdoor re-skins.

This actor turns a Glassdoor company URL (or a search keyword) into a structured dataset of reviews — title, rating, pros, cons, author, and date — pulled from public Glassdoor company pages. Pagination handled automatically.

Who uses this

• Talent-acquisition teams benchmarking their employer brand against direct competitors before a recruiting push.
• HR consultants building workforce-sentiment reports for clients in a specific industry.
• Market researchers analyzing how companies in a sector are perceived internally.
• Job seekers with tooling who want to compare 20 companies side-by-side instead of clicking through each page.
• Investment analysts using employee sentiment as a leading indicator for operational health.

What you get per review

Real fields actually pushed by the actor (verified against src/main.js):

Field	Type	Example
companyName	string	"Stripe"
title	string	"Great place for strong engineers"
rating	number / null	4.5
pros	text	"Clear growth paths, thoughtful peers..."
cons	text	"Comp lag for senior ICs since 2024 freeze..."
text	text	concatenated pros + cons
author	string	"Senior Software Engineer" (when surfaced by Glassdoor)
datePublished	string	"2026-03-11"
scrapedAt	string (ISO 8601)	"2026-04-29T05:55:00.000Z"

Up to 9 fields per review (Methods 1 & 2), 6 fields if Method 3 fires (no rating, no author, no datePublished — embedded-JSON regex doesn't extract them). For JSON-LD path (Method 1), pros and cons are emitted as empty strings — Glassdoor's JSON-LD Review schema collapses pros/cons into a single reviewBody which lands in the text field instead.

No salary data, no interview data, no ratings breakdown by category — those are separate Glassdoor surfaces with their own anti-bot patterns; not in scope for this actor. If you need those, email and we can build a dedicated pipeline (see Custom scraping below).

Input

{
  "companyUrls": [
    "https://www.glassdoor.com/Overview/Working-at-Stripe-EI_IE671932.htm",
    "https://www.glassdoor.com/Overview/Working-at-Notion-EI_IE3641702.htm"
  ],
  "searchQueries": ["fintech startups"],
  "maxReviewsPerCompany": 100
}

• companyUrls — list of Glassdoor company URLs (Overview or Reviews pages). The actor auto-converts Overview → Reviews and walks pagination.
• searchQueries — list of keyword searches. The actor parses the search results page, picks up to 10 distinct employer links per query, then collects reviews for each.
• maxReviewsPerCompany — per-company cap. Default 30. Sensible upper bound around 200-500; the underlying maxRequestsPerCrawl is 500, so very large totals across many companies can hit that ceiling.

Honest disclosure on parameters:

• sortBy IS in input_schema.json (accepts date / relevance) and IS read by the actor — but the value is NOT applied to any URL parameter in the current implementation. Effectively it is dead at runtime; reviews come back in whatever order Glassdoor's default review page serves. We will wire this up in a future version, or in a custom build if you need it now.
• There is no country or sortReviews filter. If you want a specific country marketplace, pass that country's Glassdoor URL directly (www.glassdoor.co.uk/..., www.glassdoor.de/..., etc.).
• Apify proxy is requested via Actor.createProxyConfiguration(). On free Apify plans (where proxy isn't included) this resolves to undefined and the crawler falls back to direct fetch — Glassdoor anti-bot is more likely to fire in that mode. For reliable runs at scale, use a paid Apify plan with proxy enabled, or commission a custom build with residential-proxy routing.
• The actor does NOT proactively detect anti-bot challenges (CAPTCHA, "verify you are human", session-block). If all 3 extraction methods return 0 reviews for a URL, that is the silent indicator — re-run after 30-60 minutes from a fresh IP.

Python usage example

from apify_client import ApifyClient
import statistics

client = ApifyClient("YOUR_APIFY_TOKEN")

run_input = {
    "companyUrls": [
        "https://www.glassdoor.com/Overview/Working-at-Stripe-EI_IE671932.htm",
    ],
    "maxReviewsPerCompany": 100,
}

run = client.actor("knotless_cadence/glassdoor-reviews-scraper").call(run_input=run_input)
reviews = list(client.dataset(run["defaultDatasetId"]).iterate_items())

# Average rating + spread
ratings = [r["rating"] for r in reviews if r.get("rating") is not None]
if ratings:
    print(f"Avg rating: {statistics.mean(ratings):.2f} ({len(ratings)} reviews)")
    if len(ratings) > 1:
        print(f"Median: {statistics.median(ratings):.2f}, stdev: {statistics.stdev(ratings):.2f}")

# Quick keyword scan in pros/cons
for kw in ("layoff", "comp", "burnout", "growth"):
    hits = [r for r in reviews if kw.lower() in (r.get("text") or "").lower()]
    print(f"{kw:>8}: {len(hits)} reviews mention it")

How it works (3 extraction methods)

Glassdoor renders reviews three ways depending on session, country, and anti-bot state. The actor tries all three in order:

1. JSON-LD Review/EmployerReview blocks — fastest path, used when Glassdoor inlines structured data.
2. HTML review cards — [data-test="review-card"], .gdReview, .empReview selectors with pros/cons/rating parsers.
3. Embedded JSON in <script> tags — regex match on reviewTitle + pros + cons patterns inside Apollo state / __NEXT_DATA__.

Method 1 fails → fall through to 2 → fall through to 3. If all three return empty, the page likely served an anti-bot challenge — re-run after 30-60 minutes.

Apify proxy is used when available (Actor.createProxyConfiguration()); falls back to direct fetch if the proxy SDK isn't configured. maxConcurrency=2, requestHandlerTimeoutSecs=30, maxRequestRetries=3.

Common questions

Q: Do I need a Glassdoor account? No. The actor reads publicly visible review pages.

Q: Does it pull salary or interview data? Not in this actor. Salary and interview surfaces have separate anti-bot patterns and a different schema. Available as a custom build — see Custom scraping below.

Q: How often do reviews refresh? Live at run time. Running daily gives you reliable new-review deltas.

Q: How do I turn raw reviews into actionable insight? Common patterns: (1) keyword scan on pros/cons to find recurring themes, (2) rating trend by month to detect culture shifts, (3) date-cohort analysis (e.g. reviews from the 90 days after a re-org), (4) cross-company comparison within a sector.

Q: Is it compliant with Glassdoor's terms? This actor accesses publicly visible pages only, at rate-limited pace (maxConcurrency=2). For commercial redistribution of reviews, check Glassdoor's data-use terms with legal counsel. For internal market-research use, public-data precedent (hiQ v. LinkedIn, 9th Circuit 2022) generally applies.

Q: Why might a run return fewer reviews than expected? (a) Glassdoor served an anti-bot challenge — all three extraction methods return empty; re-run after 30-60 min. (b) Page count exceeded maxRequestsPerCrawl=500; lower the per-company cap or split the run. (c) Review cards changed CSS class names again — the JSON-LD and embedded-JSON fallbacks usually catch this, but report a broken URL via email and we'll patch the selectors.

Export integrations

• CSV / JSON / Excel / HTML (native Apify dataset download)
• Google Sheets (via Apify integration)
• Webhooks (fire on each review batch)
• S3 / GCS direct sync
• Zapier / Make.com

Related scrapers

Source	Actor	Data
Glassdoor (this)	Reviews	Workforce sentiment
Trustpilot	Customer reviews	External brand sentiment
Reddit Discussion	Discussion threads	Unfiltered opinion
Google News	News articles	Layoff / re-org / leadership signals
MCP Company Researcher	Company facts	Headcount, funding, profile

All 31 published actors free to inspect on Apify Store.

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Proof of delivery: This Glassdoor reviews scraper has 39 lifetime production runs as of May 2026. Author maintains 31 published actors (78 total) and shipped a paid 3-article series in March 2026 ($150, proxy industry). Pilot pricing locked through May 2026.

Sample request? Reply sample to spinov001@gmail.com and we'll send 2 published case-study articles within 24 hours.

Custom scraping — pilot tiers

Need data, not infrastructure. We build, you query. Three tiers:

• Pilot — $97 · 1 actor, basic config, 7-day support. Good entry point — useful for a single Glassdoor competitor benchmark or a one-off salary surface.
• Standard — $297 · custom actor + Slack/email alerts on results, 30-day support. Most workforce-analytics projects fit here.
• Premium — $797 · custom actor + dashboard + 90-day support + 1 modification round. For ongoing pipelines (monthly competitor sentiment, multi-source enrichment with LinkedIn + Blind + Reddit).

Email: spinov001@gmail.com — drop specs, schema, or target URLs and get a quote within 48h.

Proof of work: 31 published Apify scrapers (78 total in portfolio) — Trustpilot 951 runs, Reddit 82, Google News 45, Glassdoor 39, Email Extractor 107. Recently delivered a paid 3-article series for a client in the proxy industry ($150).

More tips: t.me/scraping_ai · blog.spinov.online

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Disclaimer

Designed for market-research, HR-intelligence, and academic use. Respect Glassdoor's Terms of Service, applicable data-protection law (GDPR, CCPA), and scrape publicly visible content only. Not affiliated with Glassdoor, Inc.

Honest disclosure: this scraper covers public Glassdoor company-page reviews only — no login-walled content, no salary surface, no interview surface. No country filter; sortBy is in the schema but currently dead at runtime (see Honest disclosure above). Pass a country-localized URL if needed. For any of those, request a custom build.