We extract company firmographics, employee directories, tech stack signals, and department taxonomies from LeadIQ. 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 Firmographics objects from leadiq.com. All fields typed and schema-versioned.
"domain": "acme.com", "company_name": "Acme Corp", "industry": "Enterprise Software", "employee_count": 452, "hq_location": "San Francisco, CA", "founded_year": 2014, "linkedin_url": "linkedin.com/company/acme", "revenue_range": "$50M-$100M"
| # | domain | company_name | industry | employee_count | hq_location | founded_year |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Employee Directory objects from leadiq.com. All fields typed and schema-versioned.
"profile_id": "usr_849201", "first_name": "Jane", "last_name": "Doe", "job_title": "VP of Engineering", "department": "Engineering", "seniority": "VP", "linkedin_url": "linkedin.com/in/janedoe", "location": "London, UK"
| # | profile_id | first_name | last_name | job_title | department | seniority |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Tech Stack Signals objects from leadiq.com. All fields typed and schema-versioned.
"domain": "acme.com", "technology_name": "Salesforce", "category": "CRM", "vendor": "Salesforce", "adoption_status": "Active", "detected_date": "2023-10-14", "confidence_score": 0.98
| # | domain | technology_name | category | vendor | adoption_status | detected_date |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Funding & Financials objects from leadiq.com. All fields typed and schema-versioned.
"domain": "acme.com", "latest_funding_round": "Series C", "funding_amount": 45000000, "total_funding": 82000000, "investors": "['Sequoia', 'Accel']", "valuation_range": "$500M-$1B", "last_funding_date": "2023-08-12", "currency": "USD"
| # | domain | latest_funding_round | funding_amount | total_funding | investors | valuation_range |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Department Taxonomy objects from leadiq.com. All fields typed and schema-versioned.
"domain": "acme.com", "department_name": "Sales", "headcount": 142, "headcount_growth_pct": 12.5, "leadership_contacts": "['usr_9921', 'usr_8832']", "open_roles": 14, "hiring_trend": "High", "average_tenure": 2.4
| # | domain | department_name | headcount | headcount_growth_pct | leadership_contacts | open_roles |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our LeadIQ scraper handles the entire directory structure: company profiles, paginated employee lists, and firmographic details. Built with JavaScript rendering and anti-bot circumvention.
Capture domain, industry, revenue estimates, and headcount metrics across millions of company profiles.
Extract paginated lists of employees per company, including job titles, departments, and seniority levels.
Identify software and infrastructure tools adopted by target accounts, categorised by vendor and function.
Monitor headcount growth or contraction across specific departments like Sales, Engineering, and Marketing.
Standardise geographic data for headquarters and individual employee locations into structured country and city fields.
Extract associated LinkedIn URLs for companies and individual executives to support downstream enrichment.
Track recent investment rounds, total capital raised, and lead investors for private companies.
Maintain a hash index of last-seen values. Subsequent runs only push diffs, reducing storage and processing costs.
Run one-off bulk exports or configure continuous pipelines at weekly or monthly cadences.
Brief in. Clean data out.
Provide target domains, industry filters, or company size criteria. We design the extraction schema together.
We configure Scrapy and Playwright crawlers, proxy rotation, and session management for leadiq.com.
Schema validation, null-rate checks, and sample data delivery before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
LeadIQ protects its data with rate limits and bot detection. Here is how we stay resilient.
Directory sites monitor request patterns. Our crawlers use residential ISP proxies with realistic browser fingerprints and randomised request timing trained on real user behaviour.
Company profiles and paginated lists rely on JavaScript. We run full Playwright browser sessions to trigger lazy-loading and hydrate data components.
DOM structures change without warning. Our strategy uses multiple fallback chains per field, including CSS, XPath, and regex matching.
Large enterprise profiles contain thousands of employees. We handle complex pagination states to ensure complete directory extraction.
Every run emits structured logs. We alert on null-rate spikes and coverage drops, responding before you notice.
RevOps teams automate the enrichment of Salesforce or HubSpot records with accurate firmographics and employee counts.
Strategy teams size Total Addressable Markets by filtering companies by industry, revenue, and tech stack adoption.
Product teams monitor competitor headcount growth across specific departments like engineering or sales.
Sales leaders divide territories based on accurate HQ location data and employee distribution metrics.
Growth teams build target account lists based on specific tech stack signals and funding events.
Venture capital firms track headcount velocity and leadership changes to identify early-stage investment opportunities.
"LeadIQ contains one of the most accurate B2B contact and firmographic datasets available, but extracting it at scale requires dedicated infrastructure."
Most teams underestimate the investment required. Reliable LeadIQ 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 analysis.
Everything supported by our leadiq.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 rendering and interaction flows.
We maintain pools of residential ISP proxies. Rotation happens per request to prevent rate limiting.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling and dependency management.
Data delivered to where your team already works — no new tooling required.
About leadiq.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available directory information is generally permissible. DataFlirt targets only public, non-authenticated company and employee data. We do not circumvent authentication walls to steal paid credits.
We use residential ISP proxies and request timing modelled on human behaviour. We monitor for rate spikes in real time and trigger pool rotation automatically.
Direct mobile numbers and verified emails on LeadIQ are gated behind paid account credits. We extract only the publicly visible directory data and firmographics.
Pipelines can be configured to refresh target lists weekly or monthly. Change detection ensures you only process updated records.
Our packages start at a defined list of 5,000 target domains. Contact us with your use case for a scoped quote.
Yes. We provide a sample run of up to 100 company profiles as part of the scoping process so you can validate schema fit.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off firmographic dump or a continuous tracking feed across target accounts, we build and operate the pipeline. Tell us what you need.
