Scrape job postings, salary ranges, required skills, company hiring velocity, and talent demand signals from Naukri, LinkedIn, Indeed, Glassdoor, Instahyre, and 500+ job boards. Structured labour market data for talent intelligence platforms, HR analytics, and workforce research teams.
Job board data scraping is the automated extraction of structured employment and talent demand data from online job platforms. Each job posting is a rich signal: job title, company name, location, experience requirements, salary range, required skills, preferred qualifications, job description text, posting date, and application count. Aggregating thousands of these signals daily — across multiple platforms and geographies — creates a structured view of labour market demand that is far more current and granular than any survey-based workforce data.
Job postings are leading indicators of business intent. When a company begins hiring aggressively for data engineers, it signals investment in data infrastructure before any press release confirms it. When a category of skills suddenly appears across hundreds of postings in a sector, it signals an emerging technology shift months before industry analysts write about it. Structured job data is therefore not just useful for recruitment — it is a primary intelligence source for competitive analysis, investment research, and workforce strategy.
India's job market has its own distinct platforms. Naukri, Shine, Foundit (formerly Monster India), and Instahyre operate alongside the global players LinkedIn and Indeed, and each carries different employer segments and role types. DataFlirt's scrapers cover all the major Indian platforms alongside global job boards, enabling both India-specific labour market analysis and cross-market comparisons for multinational employers and researchers.
Skills extraction is a particularly valuable dimension of job data. Raw job descriptions contain unstructured skill mentions — programming languages, frameworks, tools, certifications — that need to be parsed and normalised to be analytically useful. Our NLP pipeline extracts both explicitly listed skills and implicit skill mentions from job description text, mapping them to a standardised skills taxonomy that enables cross-company and cross-posting skills demand analysis.
Comprehensive extraction built for reliability, accuracy, and scale.
Extract job title, company, location, experience level, salary range, job description text, posting date, application deadline, and applicant count.
NLP-powered skills parsing extracts required and preferred skills from job description text and maps them to a standardised skills taxonomy.
Capture stated salary ranges, stipend data for internships, and model inferred salary benchmarks from job context signals.
Monitor posting volume by company, role, and skill over time to detect hiring surges, slowdowns, and strategic pivots.
Aggregate all active postings for a company into structured hiring profiles showing team growth, tech stack, and role distribution.
Dedicated coverage for Indian job boards (Naukri, Shine, Instahyre) alongside global platforms — with normalised data across all sources.
Every field you need, structured and ready to use downstream.
A proven process that turns any source into clean structured data — reliably.
{ "status": "success", "source": "naukri", "scraped_at": "2025-03-20T09:00:00Z", "job": { "id": "NK-2025-48210", "title": "Senior Data Engineer", "company": "Flipkart", "location": "Bengaluru, Karnataka", "experience": "5-8 years", "salary_lpa": "25-35", "skills": ["PySpark","Kafka","Airflow","AWS"], "posted_date": "2025-03-18", "applicants": 284, "remote": false } }
Built on proven open-source tools and cloud infrastructure — no vendor lock-in.
Transformer-based NLP models extract explicit and implicit skill mentions from job descriptions, normalised to a standardised skills taxonomy.
Purpose-built scrapers for Naukri, Shine, Foundit, and Instahyre handle their unique structures, authentication patterns, and data formats.
Daily posting counts tracked per company and role type to build time-series hiring velocity signals for competitive intelligence.
Distributed async architecture handles millions of daily job postings across 500+ platforms with incremental collection to minimise redundancy.
Company names normalised across platforms using entity resolution to link postings to a canonical company record across all sources.
Where salary is not stated, contextual signals — seniority, location, company size, required skills — used to model inferred compensation benchmarks.
From solo analysts to enterprise data teams — here's how organizations use this data.
Hiring decisions are made months before they show up in employment statistics. When a company posts 50 data engineering roles, it reveals investment strategy, product direction, and competitive intent before any analyst report captures it. DataFlirt gives talent intelligence teams, workforce researchers, and competitive analysts structured, daily-updated job market data — turning the world's largest continuous survey of business intent into actionable intelligence.
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Everything you need to know before getting started.
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