Employer reputation tracking

1. 01Find each company once

   Company search returns employer_id and slug for a name, with the overall rating and review count. Store the ids; they do not change.

   GET /v1/company/search?engine=glassdoor&q=stripe · results.0

   {
     "position": 1,
     "employer_id": "671932",
     "name": "Stripe",
     "slug": "Stripe",
     "logo": "https://media.glassdoor.com/sql/671932/stripe-squarelogo-1610580619584.png",
     "rating": 3.8,
     "reviews_count": 1588,
     "salaries_count": 4659,
     "jobs_count": 4,
     "url": "https://www.glassdoor.com/Overview/Working-at-Stripe-EI_IE671932.11,17.htm"
   }

2. 02Snapshot the ratings

   Company returns the overall rating, six sub-ratings, CEO approval, recommend-to-a-friend and business outlook percentages, and the review count. Store each snapshot with its date: Glassdoor shows only the current values.

   GET /v1/company?engine=glassdoor&employer_id=9079&slug=Google · company.ratings

   {
     "overall": 4.4,
     "career_opportunities": 4.2,
     "compensation_and_benefits": 4.5,
     "culture_and_values": 4.2,
     "diversity_and_inclusion": 4.4,
     "senior_management": 3.9,
     "work_life_balance": 4.2,
     "recommend_to_friend_pct": 87,
     "ceo_approval_pct": 83,
     "business_outlook_pct": 82
   }

3. 03Read the newest reviews

   latest_reviews holds the newest reviews Glassdoor shows without a login, with pros, cons, the reviewer's job title, and whether they still work there.

   GET /v1/company?engine=glassdoor&employer_id=9079&slug=Google · company.latest_reviews.0

   {
     "id": "105721641",
     "title": "Supportive benefits overshadowed by bureaucracy and negative culture shift",
     "pros": "Kind people, supportive benefits, internal mobility",
     "cons": "Bureaucratic, slow-moving, negative culture shift in the past 4 years",
     "advice": null,
     "rating": 4,
     "ratings": {
       "career_opportunities": 4,
       "compensation_and_benefits": 5,
       "culture_and_values": 4,
       "diversity_and_inclusion": 4,
       "senior_management": 4,
       "work_life_balance": 5
     },
     "job_title": "Senior UX Program Manager",
     "location": "New York, NY",
     "employment_status": "Full-time",
     "current_employee": false,
     "helpful_count": 0,
     "employer_responded": false,
     "date": "2026-09-24T08:39:19.700Z"
   }

# Monthly employer rating snapshot for you and your hiring competitors.
# Appends to employer_ratings.csv and prints the newest public reviews.
import csv, datetime, os, requests

API = "https://api.clair.im"
HEADERS = {"Authorization": f"Bearer {os.environ['CLAIR_API_KEY']}"}
EMPLOYERS = ["Google", "Microsoft", "Meta"]

def get(path, **params):
    r = requests.get(f"{API}{path}", headers=HEADERS, timeout=90,
                     params={"engine": "glassdoor", **params})
    r.raise_for_status()
    return r.json()

today = datetime.date.today().isoformat()
with open("employer_ratings.csv", "a", newline="") as f:
    out = csv.writer(f)
    for name in EMPLOYERS:
        hit = get("/v1/company/search", q=name)["results"][0]
        c = get("/v1/company", employer_id=hit["employer_id"], slug=hit["slug"])["company"]
        r = c["ratings"]
        out.writerow([today, c["name"], r["overall"], r["work_life_balance"],
                      r["compensation_and_benefits"], r["senior_management"],
                      r["ceo_approval_pct"], r["recommend_to_friend_pct"],
                      c["counts"]["reviews"]])
        for review in c["latest_reviews"]:
            print(f"{c['name']} {review['rating']}/5 {review['job_title']}: {review['title']}")

Set CLAIR_API_KEY to a key subscribed to the Glassdoor API. Each call is one request against that API's monthly quota.

• Glassdoor shows signed-out visitors only the newest few reviews, usually 3. The full review history is behind its login and is not returned, so this tracks ratings, not every review.
• Ratings move slowly at large companies with tens of thousands of reviews. Watch review_count and the sub-ratings for change.
• Sub-ratings are averages of the reviews that rated them, which is fewer than all reviews.