We extract company profiles, tech stack requirements, salary ranges, and job metadata from Hired. 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 Profiles objects from hired.com. All fields typed and schema-versioned.
"company_id": "C98234", "company_name": "Fintech Solutions Ltd", "industry": "Financial Services", "employee_count": "251-500", "funding_stage": "Series C", "headquarters": "London, UK", "remote_policy": "Hybrid", "website_url": "https://example.com"
| # | company_id | company_name | industry | employee_count | funding_stage | total_funding |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Job Listings objects from hired.com. All fields typed and schema-versioned.
"job_id": "J45902", "title": "Senior Backend Engineer", "role_type": "Engineering", "seniority": "Senior", "salary_min": 120000, "salary_max": 160000, "currency": "USD", "equity_offered": true
| # | job_id | company_id | title | role_type | seniority | salary_min |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Tech Stacks objects from hired.com. All fields typed and schema-versioned.
"company_id": "C98234", "frontend_frameworks": "['React', 'TypeScript']", "backend_languages": "['Python', 'Go']", "databases": "['PostgreSQL', 'Redis']", "infrastructure": "['AWS', 'Kubernetes']", "version_control": "Git", "last_updated": "2026-02-14T10:00:00Z"
| # | company_id | frontend_frameworks | backend_languages | databases | infrastructure | testing_tools |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Salary Data objects from hired.com. All fields typed and schema-versioned.
"job_id": "J45902", "role_category": "Software Engineering", "city": "New York", "base_salary_min": 120000, "base_salary_max": 160000, "currency": "USD", "sign_on_bonus": 15000, "relocation_offered": false
| # | job_id | role_category | city | country | base_salary_min | base_salary_max |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Interview Process objects from hired.com. All fields typed and schema-versioned.
"company_id": "C98234", "total_stages": 4, "average_duration_days": 18, "hr_screen": true, "technical_assessment": true, "take_home_assignment": false, "panel_interview": true, "offer_timeline": "48 hours"
| # | company_id | total_stages | average_duration_days | hr_screen | technical_assessment | whiteboard_session |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Hired scraper extracts deep metadata on tech companies, parsing salary ranges, equity offerings, and tech stacks with full JavaScript hydration and bot circumvention.
Extract comprehensive company profiles including funding stages, employee counts, industry categorisation, and headquarters locations.
Capture base salary minimums and maximums, equity offerings, and sign-on bonuses, properly normalised by currency.
Extract and normalise required technologies into structured arrays for frontend, backend, database, and infrastructure tools.
Track company stances on hybrid, fully remote, and timezone-constrained work arrangements across all listings.
Capture structured data on health insurance, PTO policies, retirement matching, and continuous learning budgets.
Extract the exact steps, stage counts, and expected duration of a company's technical interview process.
Categorise positions accurately by individual contributor levels, management tracks, and executive roles.
Filter and extract data specific to tech hubs across the US, UK, Canada, and remote-first jurisdictions.
Run one-off bulk exports or configure continuous pipelines at daily cadences with change-detection diffing.
Brief in. Clean data out.
Provide target geographies, role categories, or specific company lists. We design the extraction schema together.
We configure Scrapy and Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for hired.com.
Schema validation, null-rate checks, salary-outlier detection, and sample profiles before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Hired protects its data with aggressive bot detection and heavily dynamic frontend applications. Here is how we maintain reliable extraction.
Hired is built as a Single Page Application. We bypass brittle DOM scraping by intercepting the internal API responses and initial React state hydration payloads, ensuring clean JSON extraction before the browser even renders it.
Job boards aggressively block datacenter IPs. Our crawlers route requests through residential ISP proxies with realistic TLS and browser fingerprints, mimicking genuine candidate browsing behaviour to avoid rate limits.
We use multiple fallback chains per field. If the internal API structure changes, our pipeline automatically falls back to DOM parsing using CSS selectors and XPath to ensure continuous data flow.
We maintain a hash index of last-seen values per company profile and job listing. Subsequent runs only push diffs, reducing compute cost and downstream processing load for salary updates.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, missing salary fields, and coverage drops, responding quickly to maintain strict data quality standards.
HR teams and compensation analysts use Hired data to track real-time market rates for specific tech stacks and seniorities.
Companies monitor rival hiring velocity, remote work policies, and benefit packages to remain competitive in talent acquisition.
Investors and analysts track the adoption rates of new programming languages and infrastructure tools across startups.
Sales teams targeting engineering leaders use tech stack data to qualify prospects and personalise outreach.
Consultancies aggregate funding stages and hiring volume to model tech sector growth and geographical shifts.
Recruitment agencies map out total addressable markets for specific niche roles based on active company demand.
"Hired holds highly structured salary and tech stack data, but accessing it at scale requires navigating complex single-page application hydration and strict bot protection."
Most engineering teams underestimate the cost of maintaining job board scrapers. Hired relies heavily on dynamic React state and aggressive rate limiting. DataFlirt manages the infrastructure, CAPTCHA solving, and proxy rotation so you receive clean data without the operational overhead.
Everything supported by our hired.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, deduplication, and retry logic. Playwright handles JavaScript rendering, state hydration, and interaction flows.
We maintain pools of residential ISP proxies across target regions. Rotation happens per-request with sticky sessions where required to bypass rate limits.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting. All state is stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About hired.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available job postings and company profiles is generally permissible. DataFlirt targets only public, non-authenticated company, salary, and tech stack data. We do not extract personal candidate profiles or circumvent employer authentication walls.
We intercept the underlying API responses and initial state hydration payloads directly, bypassing the need to scrape the rendered DOM. When API structures change, we fall back to full Playwright browser sessions for visual extraction.
Yes. We extract the explicitly stated base salary minimums, maximums, currencies, and equity ranges provided on the public job listings and company profiles.
Full catalogue refreshes at daily cadence complete within a 4-8 hour window depending on the target scope. We can also configure hourly pipelines for specific high-priority company lists.
Yes. We parse raw text descriptions and structured tags into typed arrays categorised by function, such as frontend frameworks, backend languages, and database technologies.
Our packages start at a defined company list or geographic scope with weekly delivery. For full platform extraction or custom schema requirements, we price based on volume and delivery frequency.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off tech stack dump or a continuous salary benchmarking feed across thousands of companies, we build and operate the pipeline. Tell us what you need.
