We extract job postings, company intelligence, skill requirements, and salary brackets from Shine. 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 shine.com. All fields typed and schema-versioned.
"job_id": "SH928174", "title": "Senior Backend Engineer", "company_name": "TechCorp India", "location": "Bengaluru", "experience_req": "5-8 Years", "salary_range": "18-25 LPA", "posted_date": "2026-05-10", "work_model": "Hybrid"
| # | job_id | title | company_name | location | experience_req | salary_range |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Company Profiles objects from shine.com. All fields typed and schema-versioned.
"company_id": "C48291", "name": "TechCorp India", "industry": "IT Services", "employee_count": "1000-5000", "hq_location": "Mumbai", "active_jobs_count": 42, "rating": 4.1, "founded_year": 2012
| # | company_id | name | industry | employee_count | hq_location | website |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Search Results objects from shine.com. All fields typed and schema-versioned.
"keyword": "Python Developer", "location_filter": "Delhi NCR", "position": 3, "job_id": "SH883120", "is_promoted": true, "posted_ago": "2 days ago", "scraped_at": "2026-05-12T10:15:00Z"
| # | keyword | location_filter | position | job_id | title | company_name |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Skill & Salary Data objects from shine.com. All fields typed and schema-versioned.
"job_id": "SH928174", "primary_skills": "['Python', 'Django', 'PostgreSQL']", "secondary_skills": "['AWS', 'Docker']", "min_salary": 1800000, "max_salary": 2500000, "currency": "INR", "experience_min": 5, "experience_max": 8
| # | job_id | primary_skills | secondary_skills | min_salary | max_salary | currency |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Recruiter Insights objects from shine.com. All fields typed and schema-versioned.
"recruiter_id": "R99210", "name": "Priya Sharma", "designation": "Technical Sourcer", "company_name": "TechCorp India", "active_postings": 14, "location": "Bengaluru", "last_active": "2026-05-11", "hiring_for": "['Engineering', 'Product']"
| # | recruiter_id | name | designation | company_name | active_postings | hiring_for |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Shine scraper navigates dynamic search filters, pagination limits, and bot detection to extract structured employment data with JavaScript rendering and session management built in.
Title, responsibilities, requirements, and raw HTML descriptions scraped at the job ID level.
Extract and parse min/max salary ranges, converting LPA or Thousands into standard numeric formats.
Capture primary and secondary skill requirements exactly as tagged by the recruiter.
Extract hiring volume, industry classification, and company descriptions across all active employer profiles.
Navigate deep search results past standard UI limits using backend API endpoints and parameter manipulation.
Identify organic vs sponsored job placements to track employer advertising spend.
Categorise roles by specific city, state, or work-from-home status.
Extract hiring manager names, designations, and active posting counts where public.
Track posting dates and application deadlines to flag or filter inactive listings.
Run daily or weekly pipelines to track new openings and closed roles with change-detection diffing.
Brief in. Clean data out.
Provide target keywords, locations, industries, or company names. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, and session management for shine.com.
Schema validation, null-rate checks, salary outlier detection, and sample jobs before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Job boards aggressively protect their listings. Here is how we maintain reliable extraction without triggering rate limits or IP bans.
Shine uses standard WAF and rate limiting. We route requests through Indian residential IPs with rotated TLS fingerprints to blend with regular job seeker traffic.
Shine relies heavily on client-side rendering. We intercept the backend API calls and hydrate the Next.js state directly, bypassing fragile DOM parsing for core job data.
The UI restricts users to a limited number of search result pages. We manipulate search parameters, date filters, and location bounds to extract the full corpus without hitting pagination walls.
Job descriptions vary wildly depending on the recruiter's formatting. We use multiple fallback chains and regex patterns to reliably extract salary and skill data from unstructured text blocks.
To track hiring velocity, we maintain a hash index of active jobs. Subsequent runs only push new listings or status changes, reducing downstream compute costs.
Economic researchers and government bodies track hiring trends, skill demand, and salary inflation across specific Indian states and industries.
Enterprises monitor rival hiring velocity to identify strategic shifts, new department formations, or geographic expansion plans.
Bootcamps and universities analyse skill frequency in job postings to align their training programs with current market demand.
Recruitment agencies and HR software vendors identify companies actively hiring to target their sales outreach.
HR departments aggregate compensation data across roles and cities to ensure their offers remain competitive in the current market.
Private equity firms use job posting volume as a proxy for company growth and financial health during due diligence.
"Shine holds critical signals about the Indian labour market, but extracting structured intelligence from unstructured job descriptions requires dedicated infrastructure."
Building a reliable job board scraper means dealing with inconsistent formatting, aggressive rate limits, and deep pagination walls. DataFlirt absorbs that complexity, delivering clean, normalised employment data so your team can focus on analysis rather than proxy rotation.
Everything supported by our shine.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 retry logic. Playwright handles JavaScript rendering and API interception for Next.js payloads.
We maintain pools of residential ISP proxies specifically located in India to ensure high success rates and low latency against regional WAF rules.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting.
Data delivered to where your team already works — no new tooling required.
About shine.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available job postings is generally permissible under Indian law. DataFlirt targets only public, non-authenticated job and company data. We do not extract candidate resumes or bypass employer login walls.
We use Indian residential ISP proxies and realistic request timing. We also intercept backend API calls directly to minimize the number of requests required per job posting.
Yes. When standard salary fields are empty, we use regex patterns to parse the raw HTML description for common Indian salary formats like LPA or CTC.
We can configure pipelines to run hourly for specific keywords or companies. Full category refreshes typically run on a daily or weekly cadence depending on volume.
Yes. By maintaining a hash index of active jobs, we can flag listings that disappear from search results or return 404s, emitting a closed status in the diff payload.
No. Candidate profiles and resumes are gated behind employer login walls and contain PII. We strictly scrape public job postings and company intelligence.
Our smallest packages start at a defined set of target companies or search keywords with weekly delivery. Contact us for a scoped quote based on your exact data volume.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off dump of tech jobs in Bengaluru or a continuous feed of competitor hiring activity, we scope, build, and operate the pipeline. Tell us what you need.
