Glassdoor Employer Data — Try the interactive demo

Resolve any employer name or keyword to its Glassdoor company record. Pass query (e.g. google) and receive data, a ranked array of matching employers. Each hit carries the company_id token used by every other endpoint in this project, plus name, rating, review_count, salary_count, job_count, headquarters_location, logo, company_size, industry, website and company_link. Set limit (1-100, default 10) to control how many hits come back. This is the front door of the dataset: run it once to resolve a name, then feed company_id into the overview, reviews, salaries, jobs and interviews endpoints. Verified live: a search for google returns Google (company_id 9079) first with a 4.4 rating and Mountain View headquarters.

Full profile for one employer. Pass company_id (the numeric ID from company-search; an exact company name also resolves) and receive data with company_id, name, rating, review_count, salary_count, job_count, headquarters_location, logo, company_size, company_description, industry, website, company_type, revenue, year_founded, stock ticker and the CEO's name plus approval score. Also included: sub-ratings for work-life balance, culture and values, career opportunities, compensation and benefits, senior management, diversity and inclusion, recommend-to-a-friend and business outlook, a competitors list, best-places-to-work award history, and links to the employer's reviews, jobs and FAQ pages. An unknown ID returns HTTP 200 with status OK and no data field — treat a missing data key as the not-found signal.

Paginated employee reviews for one company. Pass company_id and receive data.reviews, where each review carries review_id, summary, pros, cons, a 1-5 rating, job_title, review_datetime, employment_status, is_current_employee, years_of_employment, location, helpful counts and per-category sub-ratings (work-life balance, culture and values, compensation, senior management, diversity and inclusion, career opportunities) plus CEO-approval, recommend-to-friend and business-outlook labels. Filter by review keyword (query), language (comma list such as en,es), employment_statuses (REGULAR, INTERN, PART_TIME, CONTRACT, FREELANCE), or current employees only; sort by POPULAR, MOST_RECENT, HIGHEST_RATING or LOWEST_RATING; page 10 reviews at a time with page. Set extended_rating_data=true to attach rating-breakdown aggregates. data also reports the company rating, review_count, filtered_review_count and page_count for pagination math. Verified live: Google's newest review was dated 2026-10-06 across 6,588 pages.

Global job search across current Glassdoor postings. Pass query (role or keyword) and location (city, state or country; combine with location_type for precision) and receive data.total_count plus data.jobs, where each listing carries job_id, job_title, company_id, company_name, company_logo, location_name, location_id, job_link, easy_apply, age_in_days, the employer's rating and a salary range (min/median/max with currency and period) when disclosed. Filter by date_posted (today, 3days, week, 2weeks or month), remote_only, easy_apply_only and min_company_rating. Results page via an opaque cursor: send data.cursor back in the cursor parameter for the next page, and set limit (1-100) for page size. Verified live: front end developer in New York returned 389 open roles.

Every current opening at one employer. Pass company_id and receive data.total_count plus data.jobs (40 per page), where each listing carries job_id, job_title, company_name, company_logo, location_name, job_link, easy_apply, age_in_days and a salary range (min/max with currency and period) when the employer discloses pay. Restrict the feed to a job_function (engineering, sales, finance and 20 more categories), to a location with location_type, or to fresh postings with max_age_days; sort by MOST_RELEVANT or MOST_RECENT and page through with page. Pair with company-search to keep a watchlist of employer IDs fresh. Verified live: Google listed 3,822 open roles.

Pay benchmark for one role at one employer. Pass company_id and job_title (e.g. software engineer), plus an optional location with location_type and a years_of_experience band, and receive a single data estimate: min_salary, median_salary and max_salary for total pay, min/median/max_base_salary and min/median/max_additional_pay splits, salary_period (e.g. YEAR), salary_currency (e.g. USD), the salary_count of employee reports behind the figure and a confidence label (e.g. CONFIDENT). Verified live: a software-engineer estimate at Google resolved nationally with more than 51,000 reports and a CONFIDENT grade. Use it for like-for-like comparisons at a specific company; use salary-estimate for company-agnostic market rates.

The full salary surface for one employer as a sorted, paginated list — the complement to the single-estimate company-salaries endpoint. Pass company_id and optionally narrow by job_title or location (with location_type); sort by MOST_SALARIES (roles with the most reports), HIGH_TO_LOW (highest pay first) or MOST_RECENT (freshest reports first), and page through the list with page. Rows follow the per-role salary layout of the employer's public salaries page, so the feed drops straight into distribution charts, spreadsheets and trend trackers without querying one title at a time.

Company-agnostic pay estimate for any job title in any location. Pass job_title and location (both required), plus an optional years_of_experience band and location_type for precision, and receive an estimated pay range for that role in that market with the salary period and currency. Unlike company-salaries, no company_id is needed, so it answers open questions like what a marketing assistant makes in Chicago before an employer shortlist exists. Chain it with company-salaries to contrast the market rate against a specific employer's pay.

Candidate-reported interview experiences for one company. Pass company_id and receive data.interviews, where each report carries interview_id, job_title, location, process_description, difficulty, experience (POSITIVE, NEUTRAL or NEGATIVE), outcome, duration_days, interview_datetime, review_datetime, advice, declined_reason, helpful counts and the actual questions candidates were asked. Filter by job_title, location, job_function or received_offer_only; sort by POPULAR, MOST_RECENT, OLDEST, EASIEST or MOST_DIFFICULT; page with page and page_size (1-100; beyond page 1000 the source caps pages at 5 items). Aggregate stats ride along: total_count, average interview_difficulty, experience and hiring-channel distributions, interview_question_count and newest_review_date, so dashboards can show freshness at a glance. Verified live: Google returned 26,187 reports with an average difficulty of 3.4.