We extract internship postings, stipend bands, skill requirements, and company profiles from Internshala. 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 Internship Listings objects from internshala.com. All fields typed and schema-versioned.
"listing_id": "INT-94821", "title": "Software Development Engineering", "company_name": "TechCorp India", "is_wfh": false, "duration_months": 6, "stipend_min": 15000, "stipend_max": 25000, "ppo_available": true, "applicants_count": 342
| # | listing_id | title | company_name | location | is_wfh | duration_months |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Entry-Level Jobs objects from internshala.com. All fields typed and schema-versioned.
"job_id": "JOB-11204", "title": "Junior Data Analyst", "company_name": "DataWorks Solutions", "ctc_min": 400000, "ctc_max": 600000, "experience_required": "0-2 years", "probation_duration": 3, "openings": 4
| # | job_id | title | company_name | ctc_min | ctc_max | location |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Company Profiles objects from internshala.com. All fields typed and schema-versioned.
"company_id": "COMP-4921", "name": "FinTech Innovators", "industry": "Financial Services", "website": "https://fintechinnovators.in", "total_internships_posted": 45, "active_listings": 3, "location_hq": "Mumbai, Maharashtra"
| # | company_id | name | logo_url | description | industry | website |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Skill Requirements objects from internshala.com. All fields typed and schema-versioned.
"listing_id": "INT-94821", "listing_type": "internship", "skill_name": "Python", "category": "Programming", "is_mandatory": true, "date_added": "2026-05-10"
| # | listing_id | listing_type | skill_name | category | is_mandatory | company_id |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Search & Category Data objects from internshala.com. All fields typed and schema-versioned.
"search_keyword": "marketing", "category": "Digital Marketing", "wfh_filter": true, "total_results": 1204, "page_number": 1, "scraped_at": "2026-05-12T08:14:00Z"
| # | search_keyword | category | location_filter | wfh_filter | total_results | page_number |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our Internshala scraper handles dynamic search filters, pagination limits, and unstructured stipend formats. We deliver clean, normalised data for every internship and fresher job on the platform.
Capture titles, descriptions, roles, responsibilities, and application deadlines for both internships and full-time fresher jobs.
Parse unstructured text into clean numeric ranges for stipends, performance incentives, and full-time CTC bands.
Extract company descriptions, industry tags, website URLs, and historical hiring volume directly from employer profiles.
Accurately classify remote, hybrid, and in-office roles, extracting specific city arrays for on-site requirements.
Extract and categorise requested skills, mapping them to specific roles to track emerging entry-level tech and business stacks.
Monitor the number of applicants per listing over time to gauge demand and talent supply for specific roles.
Identify internships offering Pre-Placement Offers (PPO) and track the associated probation periods and conversion salaries.
Extract internship length in months and parse part-time versus full-time working hour requirements.
Run daily diffs to capture new postings, closed listings, and changes in applicant counts without duplicating historical records.
Brief in. Clean data out.
Provide target categories, locations, or specific company names. We map the extraction schema to your requirements.
We configure Scrapy crawlers, handle token-based pagination, and write custom parsers for stipend normalisation.
Schema validation, null-rate checks on critical fields like CTC, and location standardisation before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Extracting job data at scale requires handling dynamic API responses and unstructured text. Here is how we maintain pipeline stability.
Internshala relies on internal APIs for search results. We simulate browser requests, manage session tokens, and bypass artificial pagination limits to extract complete category depths.
Stipends appear in formats like '10000-15000 /month', '5000 /week', or 'Unpaid'. Our custom parsers normalise these strings into standard numeric minimum and maximum fields with unified monthly currencies.
We maintain state across daily runs, marking listings as closed when they disappear from search or reach their deadline, ensuring your dataset reflects the live hiring market.
To prevent IP bans during high-volume category sweeps, we route requests through Indian residential proxy pools, mimicking standard applicant browsing behaviour.
If Internshala updates its listing structure or adds new fields like specific diversity hiring tags, our pipeline detects the schema drift and alerts our engineering team immediately.
HR teams and recruiters track prevailing stipend rates and fresher CTCs across different cities and roles to remain competitive.
Bootcamps and training institutes monitor skill demands to align their curriculum and target companies actively hiring juniors.
Companies track competitor hiring volume, department expansion, and remote work policies through active job listings.
Job boards and university placement cells ingest structured feeds of relevant internships to display to their student base.
Researchers analyse entry-level hiring trends, WFH adoption rates, and regional job creation metrics.
Analysts map the frequency of specific software tools and languages in job descriptions to forecast technological adoption trends.
"Internshala holds the definitive dataset for entry-level hiring and stipend benchmarks in India, but extracting it requires parsing complex dynamic filters and unstructured text."
Most teams underestimate the investment required: reliable Internshala scraping requires residential proxies, token management for their internal APIs, custom parsers for compensation formats, and daily selector maintenance. DataFlirt absorbs that complexity so your engineers can focus on the analysis, not the infrastructure.
Everything supported by our internshala.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 fast API extraction for listings, while Playwright manages session generation and complex dynamic rendering when required.
We route requests through Indian residential IPs to avoid location-based blocking and maintain high extraction concurrency without triggering rate limits.
Pipelines run on Kubernetes. Airflow handles daily scheduling and delta diffing. All state and historical listing data is stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About internshala.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available job postings and company profiles is generally permissible. DataFlirt extracts only public, non-authenticated listing data. We do not extract private student profiles, circumvent employer authentication, or violate PII regulations.
Yes. We maintain historical state. When a listing is removed from search results or passes its application deadline, we flag it as closed rather than deleting the record, preserving your historical dataset.
We use custom Python parsers to evaluate text fields. Formats like '10k-15k/month' or 'Performance based' are mapped into strict minimum and maximum integer fields, alongside a standard stipend_type string.
Absolutely. Pipelines can be configured to scrape the entire platform, or restricted to specific search parameters, categories, or geographic locations to minimise data volume and cost.
For platform-wide extraction, we typically run daily delta pipelines. For specific high-priority categories, we can configure hourly runs to capture new postings as they go live.
We begin building your historical dataset from the day the pipeline is commissioned. We do not maintain a pre-scraped historical database of Internshala for immediate purchase.
Yes. We provide a sample extraction of up to 500 listings during the scoping phase, allowing you to validate our stipend normalisation and schema fit before committing.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a daily feed of new tech internships or a comprehensive snapshot of entry-level hiring across India — we scope, build, and operate the pipeline. Tell us what you need.
