We extract public company profiles, employee directories, and firmographic signals from Lusha. 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 lusha.com. All fields typed and schema-versioned.
"company_name": "Acme Corp", "domain": "acmecorp.com", "industry": "Software Development", "employee_count": "501-1000", "revenue_range": "$50M-$100M", "founded_year": 2012, "hq_location": "San Francisco, CA"
| # | company_id | company_name | domain | industry | employee_count | revenue_range |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Employee Directory objects from lusha.com. All fields typed and schema-versioned.
"full_name": "Jane Doe", "job_title": "VP of Engineering", "department": "Engineering", "seniority_level": "VP", "company_name": "Acme Corp", "location": "New York, NY", "linkedin_profile": "linkedin.com/in/janedoe"
| # | profile_id | full_name | job_title | department | seniority_level | company_name |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Firmographics objects from lusha.com. All fields typed and schema-versioned.
"company_name": "Acme Corp", "sic_code": "7371", "naics_code": "541511", "funding_total": "$120M", "latest_funding_date": "2024-02-15", "company_type": "Privately Held"
| # | company_name | domain | sic_code | naics_code | technologies_used | funding_total |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Locations objects from lusha.com. All fields typed and schema-versioned.
"company_name": "Acme Corp", "hq_city": "San Francisco", "hq_state": "CA", "hq_country": "United States", "phone_number": "+1-415-555-0198", "region": "North America"
| # | company_name | hq_address | hq_city | hq_state | hq_country | branch_locations |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Tech Stack objects from lusha.com. All fields typed and schema-versioned.
"company_name": "Acme Corp", "category": "Cloud Hosting", "technology_name": "Amazon Web Services", "first_detected": "2021-06-12", "last_detected": "2025-10-01", "confidence_score": 0.98
| # | company_name | domain | category | technology_name | implementation_status | first_detected |
|---|---|---|---|---|---|---|
| 1 | ||||||
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| 3 |
Our Lusha scraper handles the public directory layer: company profiles, firmographics, and public employee listings - with JavaScript rendering, session management, and anti-bot circumvention built in.
Extract domain, description, founded year, and social links for millions of B2B organisations.
Capture public employee names, job titles, departments, and seniority levels linked to target companies.
Track NAICS codes, SIC codes, company type, and operating status across the global directory.
Scrape public technology tags associated with company profiles to build technographic segments.
Monitor total funding amounts, latest round dates, and key investors listed on company profiles.
Extract primary headquarters addresses, regional branches, and associated phone numbers.
Normalise industry tags and sub-categories to align with your internal CRM taxonomies.
Capture categorical revenue brackets and employee count ranges for territory planning.
Run one-off bulk exports or configure continuous pipelines at weekly or monthly cadences.
Brief in. Clean data out.
Provide domain lists, industry filters, or company names. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for lusha.com.
Schema validation, null-rate checks, data type enforcement, and sample profiles before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
B2B directories invest heavily in scraping detection. Here is how we stay resilient - and why teams choose managed infrastructure over DIY.
Directory sites use advanced bot detection operating on TLS fingerprints and IP reputation. Our crawlers use residential ISP proxies with realistic browser fingerprints and full cookie session management.
Lusha profiles are heavily JavaScript-rendered. We run full Playwright browser sessions with JavaScript execution and lazy-load triggering to capture data that headless HTTP clients miss entirely.
DOM structures change frequently. Our selector strategy uses multiple fallback chains per field - CSS selectors, XPath, and text-pattern matching - so a layout change does not break your data pipeline.
For large company catalogues, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs - reducing compute cost and downstream processing load.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, schema drift, and coverage drops - and respond before you notice.
Revenue operations teams use firmographic data to size total addressable markets and segment accounts by headcount and revenue.
Strategy teams monitor competitor headcount growth, departmental expansion, and location footprints over time.
Marketing teams build target account lists using industry classifications, tech stack signals, and revenue brackets.
Venture capital and private equity firms track employee growth velocity and funding rounds to identify breakout companies.
Sales operations automatically append missing firmographic fields to Salesforce or HubSpot records using domain matching.
Consultancies aggregate directory data to map industry concentrations and regional business hubs.
"Lusha aggregates critical B2B contact and firmographic signals - but extracting this directory data at scale requires dedicated scraping infrastructure."
Most teams underestimate the investment required: reliable Lusha scraping requires residential proxies, full JavaScript rendering, CAPTCHA handling, daily selector maintenance, and anomaly monitoring. DataFlirt absorbs that complexity so your engineers can focus on the data - not the infrastructure.
Everything supported by our lusha.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, cookie sessions, and interaction flows.
We maintain pools of residential ISP proxies. Rotation happens per-request with sticky sessions where required. IP score monitoring prevents blacklisted pool contamination.
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 lusha.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available directory information is generally permissible under applicable law in India, the US, and the UK - reinforced by the hiQ v. LinkedIn ruling. DataFlirt targets only public, non-authenticated company profiles and directory data. We do not extract personal data behind paywalls or violate GDPR.
No. We only scrape public directory information visible without an authenticated account. We do not consume Lusha credits or extract paywalled contact information.
We use residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour. Our selectors have multi-layer fallback chains.
Full directory refreshes complete within a defined weekly or monthly window depending on the scale of the target list.
Yes. Every pipeline run produces timestamped snapshots. We maintain a time-series table per company domain for employee count brackets and revenue ranges.
Our smallest packages start at a defined domain list (typically 10,000-50,000 domains) with weekly delivery. Contact us with your use case for a scoped quote.
Absolutely. We provide a sample run of up to 500 company profiles as part of the pre-engagement scoping process - so you can validate schema fit and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off company directory dump or a continuous firmographic feed across 500K domains - we scope, build, and operate the pipeline. Tell us what you need.
