We extract senior management job postings, recruiter details, salary bands, and company profiles from iimjobs.com. 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 iimjobs.com. All fields typed and schema-versioned.
"job_id": "148291", "title": "VP Engineering", "company_name": "Confidential", "location": "Bengaluru", "experience_min": 12, "experience_max": 18, "posted_date": "2026-05-10", "industry": "Internet / E-Commerce", "function": "Technology"
| # | job_id | title | company_name | location | experience_min | experience_max |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Company Profiles objects from iimjobs.com. All fields typed and schema-versioned.
"company_id": "C9281", "name": "Fintech Innovators", "website": "https://example.com", "industry": "Financial Services", "headquarters": "Mumbai", "active_jobs_count": 14, "employee_count": "500-1000"
| # | company_id | name | website | industry | employee_count | headquarters |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Recruiter Data objects from iimjobs.com. All fields typed and schema-versioned.
"recruiter_id": "R4821", "name": "Priya Sharma", "designation": "Talent Acquisition Lead", "company": "TechCorp", "jobs_posted": 8, "last_active": "2026-05-11", "location": "Delhi NCR"
| # | recruiter_id | name | designation | company | jobs_posted | last_active |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Salary & Experience objects from iimjobs.com. All fields typed and schema-versioned.
"job_id": "148291", "min_experience": 12, "max_experience": 18, "min_salary": 4500000, "max_salary": 6500000, "currency": "INR", "equity_offered": true, "variable_pay": "20%"
| # | job_id | min_experience | max_experience | min_salary | max_salary | currency |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Search Results objects from iimjobs.com. All fields typed and schema-versioned.
"keyword": "product manager", "category": "Product Management", "position": 1, "job_id": "149021", "title": "Senior Product Manager", "company": "GrowthStartup", "location": "Bengaluru", "featured_badge": true
| # | keyword | category | position | job_id | title | company |
|---|---|---|---|---|---|---|
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Our iimjobs scraper targets the core data entities: premium job listings, confidential mandates, recruiter profiles, and salary bands — with session management and proxy rotation built in.
Title, company, location, experience requirements, salary ranges, and full descriptions — scraped at the job ID level.
Track active job counts, departmental focus, and location expansion for specific companies over time.
Extract recruiter names, designations, active job counts, and contact mapping where publicly available.
Normalise stated salary ranges into structured min/max integer fields for direct database ingestion.
Standardise multi-city job postings into discrete location arrays for accurate geographical filtering.
Capture iimjobs' specific industry and functional tags to align with your internal taxonomy.
Monitor posting dates and application deadlines to calculate time-to-fill metrics for senior roles.
Identify and flag confidential or agency-posted roles versus direct company hires.
Run one-off bulk exports or configure continuous pipelines at hourly or daily cadences with change-detection diffing.
Brief in. Clean data out.
Provide job categories, locations, keywords, or company lists. We design the extraction schema together.
We configure Scrapy crawlers, proxy rotation, session management, and parsing logic for iimjobs.com.
Schema validation, null-rate checks, salary parsing verification, and sample data review before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Job boards protect their inventory. Here's how we ensure reliable delivery of premium talent data.
Job boards monitor IP velocity to block scrapers. Our crawlers use residential ISP proxies with realistic browser fingerprints and randomised request timing to bypass rate limits.
Certain recruiter details and salary bands require a logged-in session. We manage cookie jars and session rotation to access gated fields without triggering account bans.
DOM structures change. Our selector strategy uses multiple fallback chains — CSS selectors, XPath, and text-pattern matching — ensuring uninterrupted data flow.
We maintain a hash index of last-seen values per job. 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 and schema drift, responding before you notice.
Recruitment agencies map senior talent movement and active mandates across competitors.
Strategy teams monitor competitor hiring velocity in specific functions to predict product roadmaps.
HR departments aggregate salary bands for specific roles and experience levels to optimise compensation structures.
B2B sales teams target active recruiters and HR heads identified through fresh job postings.
Consultancies track industry-wide hiring shifts, remote work adoption, and emerging skill requirements.
Job aggregators and career platforms enrich their internal databases with premium management roles.
"Iimjobs holds the most concentrated dataset of premium management roles and salary bands in India — but accessing it programmatically requires dedicated infrastructure."
Most teams underestimate the investment required: reliable iimjobs scraping requires residential proxies, handling session expiry, bypassing rate limits, and daily selector maintenance. DataFlirt absorbs that complexity so your engineers can focus on the analysis — not the infrastructure.
Everything supported by our iimjobs.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 execution and cookie sessions for authenticated routes.
Pools of residential ISP proxies ensure high success rates against rate limiters. IP score monitoring prevents blacklisted pool contamination.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling and dependency management. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About iimjobs.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available job postings is generally permissible. DataFlirt extracts non-authenticated, public job and company data. We do not extract private candidate resumes or violate platform terms regarding user data. Clients should consult legal counsel for specific use cases.
We use residential ISP proxies and request timing modelled on human behaviour to distribute load. Our infrastructure monitors response codes and automatically rotates IPs upon detecting blocks.
Pipelines can be configured for daily or hourly runs depending on your requirements. Change detection ensures you receive the latest job postings and status updates promptly.
Yes. We monitor active job lists and flag records when a previously seen job ID is no longer accessible or marked closed by the platform.
Our packages start at defined category or company lists with weekly delivery. We price based on volume and delivery frequency. Contact us for a scoped quote.
Yes. We provide a sample run of up to 500 job postings as part of the pre-engagement scoping process to validate schema fit and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a full dump of current premium mandates or continuous tracking of competitor hiring — we scope, build, and operate the pipeline. Tell us what you need.
