We extract company reviews, salary reports, interview questions, and job postings from Glassdoor. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Company Reviews objects from glassdoor.com. All fields typed and schema-versioned.
"review_id": "empReview_849201", "company_name": "Stripe", "overall_rating": 4.5, "employment_status": "Current Employee", "pros": "Great engineering culture and compensation.", "cons": "High workload during product launches.", "recommend_to_friend": true
| # | review_id | company_id | company_name | employee_title | employment_status | location |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Salary Reports objects from glassdoor.com. All fields typed and schema-versioned.
"job_title": "Senior Software Engineer", "company_name": "Stripe", "base_pay_mean": 185000, "base_pay_min": 160000, "base_pay_max": 210000, "currency": "USD", "report_count": 42
| # | salary_id | company_name | job_title | location | pay_period | base_pay_mean |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Interview Questions objects from glassdoor.com. All fields typed and schema-versioned.
"job_title": "Data Scientist", "offer_status": "Accepted", "experience_rating": "Positive", "difficulty_rating": 3.8, "questions_asked": "['Explain a random forest model.']", "interview_process": "Phone screen followed by 4 onsite rounds."
| # | interview_id | company_name | job_title | interview_date | offer_status | experience_rating |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Job Listings objects from glassdoor.com. All fields typed and schema-versioned.
"job_id": "jl_10029384", "job_title": "Backend Engineer", "location": "London, UK", "remote_status": "Hybrid", "salary_estimate_min": 80000, "salary_estimate_max": 110000
| # | job_id | company_name | job_title | location | remote_status | salary_estimate_min |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Company Overview objects from glassdoor.com. All fields typed and schema-versioned.
"name": "DataFlirt", "hq_location": "Bengaluru, India", "size": "51 to 200 Employees", "industry": "Information Technology", "overall_rating": 4.8, "ceo_name": "John Doe"
| # | company_id | name | website | hq_location | size | founded_year |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Glassdoor scraper handles every layer of the platform: company profiles, salary bands, interview experiences, and the review corpus, with session management and anti-bot circumvention built in.
Extract pros, cons, advice to management, and sub-ratings for work-life balance, culture, and career opportunities.
Capture base pay, cash bonuses, stock options, and profit sharing across different roles and geographic locations.
Collect specific interview questions, difficulty ratings, offer statuses, and process descriptions submitted by candidates.
Extract full job descriptions, Glassdoor salary estimates, remote work policies, and employer types.
Track granular metrics on executive leadership approval and overall business outlook trajectory.
Extract employee ratings and qualitative feedback on healthcare plans, PTO, and retirement matching.
Monitor demographic sentiment and specific D&I ratings provided by current and former employees.
Target glassdoor.com, glassdoor.co.uk, glassdoor.co.in, and other regional domains from a unified schema.
Run one-off bulk exports or configure continuous pipelines at weekly or monthly cadences with change-detection diffing.
Brief in. Clean data out.
Provide company URLs, job titles, or geographic regions. We design the extraction schema together.
We configure Scrapy crawlers, residential proxy rotation, session management, and CAPTCHA handling for glassdoor.com.
Schema validation, null-rate checks, and sample data review before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Glassdoor invests heavily in scraping detection and data gating. Here is how we stay resilient, and why teams choose managed infrastructure over DIY.
Glassdoor uses strict bot mitigation. Our crawlers use residential ISP proxies with realistic browser fingerprints, randomised request timing, and full TLS spoofing to bypass these checks.
Glassdoor gates review pagination and salary details behind login walls. We maintain authenticated session pools with automated rotation to extract data beyond the first page.
Instead of parsing brittle DOM elements, we intercept Glassdoor's internal GraphQL responses, yielding cleaner, more structured data that is less prone to breaking when layouts change.
Glassdoor frequently tests new frontend components. Our selector strategy uses multiple fallback chains and API interception so a layout experiment does not break your data pipeline.
For large company profiles, we maintain a state index of last-seen review IDs. Subsequent runs only pull new entries, reducing compute cost and downstream processing load.
HR teams monitor their own company reviews and ratings to address negative feedback and improve employer brand perception.
Organisations track competitor salary bands, benefit ratings, and employee sentiment to remain competitive in talent acquisition.
Recruiters analyse interview questions and difficulty ratings to optimise their own hiring processes and candidate experience.
Private equity firms and hedge funds track employee sentiment and CEO approval ratings as leading indicators of company health.
Compensation analysts build regional pay models using aggregated Glassdoor salary reports across thousands of job titles.
Data science teams use the vast corpus of textual reviews to train sentiment analysis models and extract workplace trends.
"Glassdoor holds the definitive corpus of global employer sentiment and compensation data, but extracting it requires navigating strict rate limits and aggressive bot protection."
Most teams underestimate the investment required: reliable Glassdoor scraping requires residential proxies, session cookie management for gated pagination, GraphQL interception, and anomaly monitoring. DataFlirt absorbs that complexity so your engineers can focus on the analysis, not the infrastructure.
Everything supported by our glassdoor.com scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy handles crawl orchestration and deduplication. Playwright handles JavaScript execution, cookie sessions, and interaction flows required for Glassdoor authentication.
We maintain pools of residential ISP proxies. Rotation happens per-request with sticky sessions where required to prevent account flags during deep pagination.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About glassdoor.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Glassdoor is generally permissible under applicable law, reinforced by rulings like hiQ v. LinkedIn. DataFlirt targets only public company data, reviews, and aggregated salary metrics. We do not extract personal candidate profiles or violate GDPR.
Glassdoor restricts unauthenticated users from viewing beyond the first page of reviews or salaries. We utilise automated session management and authenticated proxy pools to navigate these walls and extract the complete historical dataset.
We support all regional domains including glassdoor.com, glassdoor.co.uk, glassdoor.co.in, glassdoor.ca, and glassdoor.com.au. Data is normalised into a unified schema regardless of the source region.
Yes. You can provide a list of specific job titles, companies, or geographic locations, and we will configure the pipeline to target only those intersections.
For continuous pipelines, we can configure daily or weekly runs to capture new reviews and updated salary bands. Historical backfills are executed once and updated incrementally.
Absolutely. We provide a sample run of up to 10 company profiles as part of the pre-engagement scoping process, allowing you to validate schema fit and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a historical dump of competitor reviews or a continuous feed of salary bands, we scope, build, and operate the pipeline. Tell us what you need.
