We extract job listings, department structures, office locations, and custom application fields from Greenhouse boards. 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 Job Postings objects from greenhouse.io. All fields typed and schema-versioned.
"req_id": "req_84921", "title": "Senior Backend Engineer", "department": "Engineering", "location": "London, UK", "remote_status": "Hybrid", "employment_type": "Full-time", "salary_min": 85000, "salary_max": 110000, "currency": "GBP"
| # | req_id | internal_job_id | title | department | location | remote_status |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Department Data objects from greenhouse.io. All fields typed and schema-versioned.
"department_id": "dept_402", "department_name": "Data Infrastructure", "parent_department_id": "dept_101", "parent_department_name": "Engineering", "board_token": "dataflirt", "active_job_count": 14, "cost_center": "CC-ENG-04"
| # | department_id | department_name | parent_department_id | parent_department_name | board_token | active_job_count |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Location Data objects from greenhouse.io. All fields typed and schema-versioned.
"office_id": "loc_883", "office_name": "Bengaluru HQ", "city": "Bengaluru", "state": "Karnataka", "country": "India", "region": "APAC", "is_remote": false, "timezone": "Asia/Kolkata"
| # | office_id | office_name | city | state | country | region |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Application Fields objects from greenhouse.io. All fields typed and schema-versioned.
"field_id": "custom_49201", "field_name": "LinkedIn Profile URL", "field_type": "url", "is_required": true, "options_list": "[]", "job_id": "req_84921", "board_token": "dataflirt"
| # | field_id | field_name | field_type | is_required | options_list | job_id |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Board Metadata objects from greenhouse.io. All fields typed and schema-versioned.
"board_token": "dataflirt", "company_name": "DataFlirt", "logo_url": "https://boards.greenhouse.io/dataflirt/logo.png", "total_active_jobs": 42, "departments_count": 8, "locations_count": 3, "scrape_timestamp": "2026-08-14T10:22:15Z"
| # | board_token | company_name | logo_url | total_active_jobs | departments_count | locations_count |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Greenhouse scraper targets the underlying API structures and custom domain implementations to extract clean, normalised job and department data across thousands of companies.
Extract title, rich text description, requirements, employment type, and custom metadata fields per requisition.
Identify hidden Greenhouse boards via footprinting and standardise custom domain implementations back to a unified schema.
Reconstruct nested department hierarchies to understand organisational structure and hiring focus areas.
Parse pay transparency data, extracting minimum and maximum salary ranges along with currency codes.
Standardise location strings and extract explicit remote, hybrid, or on-site designations.
Extract custom application questions and requirements configured by the employer on a per-job basis.
Track requisition lifecycle events. Identify exact dates when roles are opened, updated, or closed.
Handle international Greenhouse instances, local language postings, and region-specific compliance fields.
Run continuous pipelines that only output new, modified, or deleted roles to reduce downstream processing load.
Brief in. Clean data out.
Provide company names, domains, or Greenhouse board tokens. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, and session management for greenhouse.io endpoints.
Schema validation, null-rate checks, and department mapping verification before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Greenhouse implementations vary wildly across companies. Here is how we normalise the data and maintain pipeline stability.
Whenever possible, we target the Greenhouse embedded API (boards-api.greenhouse.io) for structured JSON. For companies using custom hosted boards or iframe implementations, we fall back to DOM parsing, normalising the output to match the API schema.
Greenhouse rate limits aggressively on single IP addresses. We distribute requests across a large pool of datacenter and residential proxies, implementing exponential backoff and jitter to stay under rate limit thresholds.
Employers frequently customise their Greenhouse boards with unique JavaScript frameworks and CSS layouts. Our selector strategy uses structural heuristics to identify job blocks, departments, and locations regardless of the visual presentation.
We maintain a hash index of active job IDs per board. Subsequent runs compare the current state against the index, emitting structured diffs for newly opened roles, updated descriptions, and closed requisitions.
Companies frequently migrate ATS platforms or change board tokens. We alert on 404 errors, sudden drops in job counts, and domain redirects, allowing us to update the target configuration before data gaps occur.
Track hiring velocity, department expansion, and strategic focus areas of competitor companies based on open requisitions.
Identify companies using specific technology stacks or expanding certain departments based on job requirements and titles.
Aggregate salary bands, remote work trends, and skill demand across thousands of high-growth companies.
Feed job boards, recruitment marketplaces, and talent networks with fresh, structured job postings.
Private equity and venture capital firms monitor startup growth signals via headcount expansion and executive hiring.
Sync ATS data for external tooling, compensation benchmarking platforms, and diversity analytics software.
"Greenhouse powers the hiring for the world's fastest-growing companies. Tracking their open requisitions is the clearest signal of corporate strategy and financial health."
Most teams underestimate the investment required to track thousands of disparate Greenhouse boards: reliable scraping requires discovering hidden board tokens, normalising custom domain implementations, handling rate limits, and standardising nested department structures. DataFlirt absorbs that complexity so your engineers can focus on the analysis, not the infrastructure.
Everything supported by our greenhouse.io scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
A hybrid approach targeting the Greenhouse embedded API for structured JSON where possible, falling back to Playwright DOM parsing for custom hosted boards.
Datacenter and residential IP rotation to bypass Cloudflare protections and distribute requests across rate limit windows.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting for thousands of concurrent board scrapes.
Data delivered to where your team already works — no new tooling required.
About greenhouse.io scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available job postings from Greenhouse is generally permissible. DataFlirt targets only public, non-authenticated job and department data. We do not extract personal candidate data, circumvent authentication walls, or access internal ATS systems.
We use domain footprinting, DNS records, and search engine dorking to identify Greenhouse board tokens and custom careers page implementations for your target company list.
Yes. Many companies host Greenhouse boards on custom domains or via iframes. Our pipeline identifies the underlying API calls or parses the custom DOM structure to extract the data into our normalised schema.
Pipelines can be configured for daily, hourly, or near real-time cadences depending on the number of boards tracked and your freshness requirements.
Yes. We extract minimum and maximum salary bands, currency codes, and equity indicators where provided in the job description or structured metadata fields.
Our minimum engagement typically starts at tracking 500 target boards with daily delivery. For larger aggregations or custom schema requirements, we price based on volume and frequency.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off dump of target companies or a continuous feed of job market data across 10,000 boards, we scope, build, and operate the pipeline. Tell us what you need.
