TeamBlind Reviews scraper

Scrape every review for any company on TeamBlind — full pros, cons, ratings, job titles, dates. One row per review, one row per company. No login required, proxies included.

How it works

✨ Why use this scraper?

Tired of TeamBlind scrapers that break every other week? Glassdoor wrappers that miss half the fields? Tools that truncate pros/cons at 200 chars?

• 📝 Full pros and cons text. No truncation, no "read more" cut-offs. Every review delivered with the same text the reviewer wrote.
• ⭐ Six ratings per review. Headline overall rating plus career, work-life balance, compensation, culture, and management — each on the 1–5 scale.
• 👤 Reviewer context. Job title (mapped from jobgroup), tenure years, location, country ISO code, current-vs-former employee flag.
• 🏢 Per-company aggregates. A second dataset row per company with logo, HQ, founded year, employee size, industry, average ratings, and reviewer city/country histogram.
• 🔁 Monitoring mode. Skip reviews already scraped in earlier runs. Useful for incremental tracking.
• ⚡ Fast by default. Built on the impit HTTP client with sliding-window concurrency and parallel pagination. ~3.6 s for 100 reviews of one company on the test machine.
• 🛡 Login-wall bypass for pros/cons. TeamBlind serves real review text only on page 1 to logged-out viewers and replaces page-2+ content with Lorem ipsum…. The scraper detects this automatically and recovers the real text from each review's permalink page. No auth needed. No flag to flip.
• 🚫 No auth, no headless browser. Pure HTTP + Cheerio. Public review pages only — no login walls, no paid features touched.

🎯 Use cases

Team	What they build
HR / Recruiting	Employer-reputation dashboards; surface what current and former employees say about pay and management.
Market research	Company-vs-company sentiment benchmarks across industries.
Competitive intel	Track tone shifts at a competitor over time (combined with monitoring mode).
Investors / analysts	Workforce-sentiment signals as a leading indicator alongside financials.
Journalists	Source-rich workplace stories with verifiable, structured pros/cons text.
Internal teams (anonymous read-back)	Pull your own company's reviews into a BI tool without manual copy-paste.

✅ Supported inputs

The scraper accepts two URL types and one shorthand:

Input	Example	Result
Listing URL	https://www.teamblind.com/company/Twilio/reviews	All reviews for Twilio, paginated through every ?page=N.
Single-review URL	https://www.teamblind.com/company/Twilio/review/z-eM7ygC89	Just that one review.
Company name shorthand	["Twilio", "Stripe", "Meta"] (via companyNames)	Built into listing URLs automatically.

Not supported (intentionally — outside client scope or behind a login):

• ❌ Salary URLs (/company/{co}/salaries)
• ❌ Benefits URLs (/company/{co}/benefits)
• ❌ Article / channel URLs (/article/{id}, /channels/{name})
• ❌ User profile URLs
• ❌ Anything requiring TeamBlind login (verified-employee discussion threads)

🔁 How the flow works

1. You provide one or more company review URLs (or just company names).
2. The scraper walks every page of reviews for each company until exhausted or maxItems is reached.
3. For each review, it captures rating, full pros/cons, job title, date, and ~25 supporting fields.
4. For each company, it writes one row to a separate companies dataset with logo, HQ, employee size, average ratings, and reviewer geography.
5. Optional monitoringMode skips reviews already collected in earlier runs.

Engine

The scraper uses the impit HTTP client (Firefox TLS fingerprint, HTTP/3 when no proxy) with a global sliding-window concurrency pattern. Page 1 is fetched first to learn totalCount, then pages 2..N are fanned out in parallel. ~3.6 s for 100 reviews on the test machine. No headless browser, no Crawlee request queue overhead.

📥 Input parameters

Start

Parameter	Type	Required	Default	Description
startUrls	array	*	—	TeamBlind URLs to scrape: listing URLs or single-review URLs.
companyNames	array	*	—	Company names (case-sensitive, exactly as in TeamBlind URLs). Each becomes https://www.teamblind.com/company/{name}/reviews.

* Either startUrls or companyNames is required.

Options

Parameter	Type	Required	Default	Description
maxItems	integer	No	1000	Hard cap on reviews scraped across all start URLs in this run.
monitoringMode	boolean	No	false	Skip reviews seen in earlier runs (per-user key-value store).
fetchReviewDetails	boolean	No	false	Make one extra HTTP call per review to its permalink. The listing already contains everything, so this is opt-in for redundancy.
maxConcurrency	integer	No	10	Maximum simultaneous page fetches.
minConcurrency	integer	No	1	Minimum simultaneous page fetches.
maxRequestRetries	integer	No	5	Retry attempts on failed requests.
proxy	object	No	Apify residential	Proxy configuration. Apify residential recommended.

📊 Output overview

The actor writes to two datasets:

• default — one row per review (~30 fields). This is the main output.
• companies — one row per company crawled (~18 fields), pushed when page 1 of that company's reviews is processed.

Both formats export to JSON and CSV. Different start URLs for the same company produce a single deduped company row.

📊 Output samples

Review row (default dataset)

Real row from https://www.teamblind.com/company/Twilio/reviews:

{
  "id": 436627,
  "urlAlias": "z-eM7ygC89",
  "rating": 3,
  "ratingsBreakdown": {
    "career": 3,
    "balance": 3,
    "compensation": 4,
    "culture": 3,
    "management": 3
  },
  "pros": "Used to be decent compensation\nOpportunity to work at scale\n\n",
  "cons": "Terrible product management\nMediocre management. Some of the most useless sr dirs and vp levels you will come across.\n\n",
  "reasonResign": null,
  "jobTitle": "Software Engineer",
  "memberJobgroupId": 7694,
  "jobFamilyId": 6,
  "reviewDate": "2026-04-29T02:32:02.000Z",
  "updatedAt": "2026-04-29T02:32:02.000Z",
  "approvedAt": "2026-05-06T02:19:18.308Z",
  "summary": "Decent pay, mediocre company",
  "helpfulCnt": 0,
  "companyResponse": null,
  "companyResponseAt": null,
  "memberLocation": "San Francisco, CA",
  "memberLocationCountryIsoCode": "US",
  "memberStart": 2020,
  "memberEnd": null,
  "isCurrentEmployee": true,
  "nickname": "u*****",
  "memberId": 2340694,
  "isPinned": "N",
  "isFeatured": null,
  "status": "A",
  "companyName": "Twilio",
  "companyPageId": 89,
  "companyChannelId": 100109,
  "sourceUrl": "https://www.teamblind.com/company/Twilio/review/z-eM7ygC89"
}

Company row (companies dataset)

{
  "urlAlias": "Twilio",
  "companyId": 100109,
  "companyName": "Twilio",
  "logoUrl": "https://static.teamblind.com/img/channel/logo/us/3ee5273a649429.png",
  "webSiteUrl": "www.twilio.com",
  "founded": "2008",
  "employeeSize": "1,001 to 5,000 employees",
  "hqLocation": "San Francisco, CA",
  "industryName": "Internet",
  "rating": "3.5",
  "ratingsBreakdown": {
    "career": "3.2",
    "balance": "4.0",
    "compensation": "3.6",
    "culture": "3.4",
    "management": "2.9"
  },
  "reviewCount": 1249,
  "cityDistribution": [
    { "name": "San Francisco, CA", "count": 498 },
    { "name": "Remote, OR", "count": 99 }
  ],
  "countryDistribution": [
    { "isoCode": "US", "name": "United States", "count": 1004 },
    { "isoCode": "IN", "name": "India", "count": 87 }
  ],
  "sourceUrl": "https://www.teamblind.com/company/Twilio/reviews"
}

🗂️ Key output fields

Review identifiers

Field	Type	Description
id	integer	TeamBlind's internal review ID. Stable across runs.
urlAlias	string	Short slug used in the review permalink (e.g. z-eM7ygC89).
sourceUrl	string	Direct permalink to the review on TeamBlind.

Ratings

Field	Type	Description
rating	integer (1–5)	Headline overall rating.
ratingsBreakdown.career	integer (1–5)	Career opportunities / growth.
ratingsBreakdown.balance	integer (1–5)	Work-life balance.
ratingsBreakdown.compensation	integer (1–5)	Pay and benefits.
ratingsBreakdown.culture	integer (1–5)	Company culture.
ratingsBreakdown.management	integer (1–5)	Quality of management.

Review content

Field	Type	Description
pros	string	Full "what's good" text. Newlines preserved. Never truncated.
cons	string	Full "what's bad" text. Never truncated.
summary	string	Reviewer-supplied one-line headline.
reasonResign	string | null	Why the reviewer left. Usually null for current employees.

Reviewer / job info

Field	Type	Description
jobTitle	string	Job title as shown on TeamBlind (mapped from jobgroup — TeamBlind's own jobTitle is always null).
memberJobgroupId	integer	Numeric job-group ID. Useful for grouping across companies.
jobFamilyId	integer	Higher-level job family ID (e.g. 6 = Engineering).
memberLocation	string	City+state or country (e.g. San Francisco, CA, Ireland).
memberLocationCountryIsoCode	string	ISO-3166 alpha-2 country code.
memberStart	integer	Year the reviewer started.
memberEnd	integer | null	Year they left. null for current employees.
isCurrentEmployee	boolean	Derived: true when memberEnd is null.
nickname	string	Anonymized nickname (e.g. u*****). TeamBlind masks all but the first character.
memberId	integer	TeamBlind's internal numeric ID for the reviewer.

Timestamps

Field	Type	Description
reviewDate	ISO 8601	When the review was posted.
updatedAt	ISO 8601	Last edit (equal to reviewDate when never edited).
approvedAt	ISO 8601	Moderation approval timestamp.

Engagement & moderation

Field	Type	Description
helpfulCnt	integer	Number of "helpful" votes.
companyResponse	string | null	Official company reply text, when present.
companyResponseAt	ISO 8601 | null	Timestamp of the company reply.
isPinned	Y / N	Pinned at the top of the company list.
isFeatured	boolean | null	Featured in TeamBlind editorial.
status	string	Moderation status (A = active).

Company references

Field	Type	Description
companyName	string	Display name of the company.
companyPageId	integer	Internal numeric page ID.
companyChannelId	integer	Internal channel ID (different from companyPageId).

Company-row fields (companies dataset)

Field	Type	Description
urlAlias, companyId, companyName	id + display	Identifiers.
logoUrl, bgImgUrl	URL	Company logo and industry banner.
webSiteUrl	string	Homepage (no protocol, e.g. www.twilio.com).
founded, employeeSize, hqLocation	string	"2008", "1,001 to 5,000 employees", "San Francisco, CA".
industryId, industryName	id + name	E.g. 247, "Internet".
description	string	Company blurb.
plan, isPremium	string	TeamBlind subscription state.
rating	string	Headline overall rating (e.g. "3.5").
ratingsBreakdown	object	Sub-rating averages, string values like "3.2".
reviewCount	integer	Total reviews on TeamBlind for this company.
cityDistribution	array	[{ name, count }, …] reviewer city histogram, sorted by count.
countryDistribution	array	[{ isoCode, name, count }, …] country histogram.
sourceUrl	string	The listing URL we crawled.

🚀 Examples

Scrape one company

{
  "startUrls": ["https://www.teamblind.com/company/Twilio/reviews"],
  "maxItems": 500
}

Scrape several companies by name

{
  "companyNames": ["Twilio", "Stripe", "Meta"],
  "maxItems": 1500
}

Pull a single review

{
  "startUrls": ["https://www.teamblind.com/company/Twilio/review/z-eM7ygC89"]
}

Incremental monitoring run

{
  "companyNames": ["Twilio"],
  "monitoringMode": true,
  "maxItems": 200
}

💻 Integrations

Python

from apify_client import ApifyClient

client = ApifyClient("YOUR_API_TOKEN")

run = client.actor("memo23/apify-teamblind-scraper").call(run_input={
    "companyNames": ["Twilio"],
    "maxItems": 100,
})

for review in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(f"{review['rating']}/5 by {review['jobTitle']} ({review['memberLocation']})")
    print(f"  Pros: {review['pros'][:80]}...")

JavaScript

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: 'YOUR_API_TOKEN' });

const run = await client.actor('memo23/apify-teamblind-scraper').call({
    companyNames: ['Twilio'],
    maxItems: 100,
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach(r => {
    console.log(`${r.rating}/5 — ${r.jobTitle} (${r.memberLocation})`);
});

💡 Tips for best results

1. Start with maxItems: 30 to test. That's roughly one page of reviews. Verify the output before running large jobs.
2. Use companyNames for batches. It's faster than typing every URL and dedupes the company-summary row automatically.
3. Skip fetchReviewDetails unless you need it. The listing payload already has every field. Enabling it doubles requests for no extra data.
4. Use monitoringMode for daily/weekly tracking. Combined with a scheduled run, it gives you only-new-reviews delivery.
5. Pair the two datasets. Join review rows on companyName to the company row to get rating context per review.

❓ FAQ

Which TeamBlind URLs are supported? Listing URLs (/company/{co}/reviews) and single-review URLs (/company/{co}/review/{alias}). Plus the companyNames shorthand which is converted to listing URLs.

Does this scrape salaries or benefits? No — only reviews. That matches the original client scope. Salary and benefits pages have different shapes and would need a separate scraper.

Are pros and cons truncated? No. The full text is returned exactly as written. Newlines are preserved.

Why was I getting "Lorem ipsum dolor sit amet…" as pros/cons in older runs? TeamBlind shows the first 30 reviews (page 1) with full content for unauthenticated viewers, then replaces every review's pros/cons with the standard Lorem ipsum… boilerplate from page 2 onwards to gate content behind a sign-in wall. Other fields (rating, job title, date, location) come back correct on every page. The scraper now detects this automatically and recovers the real pros/cons by fetching each gated review's detail page (/company/{co}/review/{alias}), which is not gated — no auth required. You'll see [bypass] N/30 reviews on this page are Lorem-gated — fetching detail pages in parallel in the logs. This adds one HTTP request per gated review, parallelized so wall-clock impact is small.

Does it require a TeamBlind login? No. The actor only hits public review pages. No verified-employee discussion threads, no login wall content.

Why are reviewer names masked like u*****? That's how TeamBlind anonymizes reviewers on the public page. We pass through what they show.

Why is the actual jobTitle field always null in the raw payload? TeamBlind has both a jobTitle field (always null on public reviews) and a jobgroup field (populated, e.g. "Software Engineer"). The actor maps jobgroup → jobTitle so the output uses the field name you'd expect.

What happens when TeamBlind changes their layout? The parser walks every script tag and finds review-shaped objects by structural matching, not by fragile component names. If the embedded payload disappears entirely, the actor logs a clear "could not locate reviews payload — TeamBlind layout may have changed" warning instead of silently producing empty rows.

Can I scrape multiple companies in one run? Yes. Pass either multiple startUrls or multiple companyNames. Each company gets its own row in the companies dataset.

What about rate limits and blocks? The actor uses Apify's session pool with proxy rotation. Apify residential proxy is recommended for production runs.

💻 Local development

git clone https://github.com/yourusername/apify-teamblind-cheerio.git
cd apify-teamblind-cheerio
npm install
npm run start:dev

Edit storage/key_value_stores/default/INPUT.json to change inputs. Outputs land in storage/datasets/default/*.json (reviews) and storage/datasets/companies/*.json (per-company aggregates), plus data.csv / data.json exports in the project root.

📬 Support

• For issues or feature requests, please use the Issues section of this actor on the Apify platform.
• Author's website: https://muhamed-didovic.github.io/
• Email: muhamed.didovic@gmail.com

Response time: usually under 24 hours.

🔧 Additional services

• Custom modifications, whole-dataset exports, scheduled deliveries: muhamed.didovic@gmail.com
• Need something else scraped? Email above.
• API services for this scraper (no Apify fee, just usage fee): contact muhamed.didovic@gmail.com

🔍 Explore more scrapers

If you found this useful, check out our other Apify actors at memo23's Apify profile — including scrapers for Dice.com, LandSearch, Bayut, and more.

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⚠️ Disclaimer

This Actor is an independent tool and is not affiliated with, endorsed by, or sponsored by Team Blind, Inc. or any of its subsidiaries. All trademarks mentioned are the property of their respective owners.

The scraper accesses only publicly available review pages — no authenticated endpoints, paid features, or content behind the TeamBlind login wall. Users are responsible for ensuring their use complies with TeamBlind's Terms of Service, applicable data-protection law (GDPR, CCPA, etc.), and any contractual obligations of their own organization.

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