We extract job postings, walk-in schedules, skill requirements, and company intelligence from TimesJobs. 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 Listings objects from timesjobs.com. All fields typed and schema-versioned.
"job_id": "TJ982341", "title": "Senior Python Backend Developer", "company_name": "TechCorp India", "location": "Bengaluru", "experience_required": "5-8 yrs", "salary_range": "15,00,000 - 25,00,000 p.a.", "skills": "['Python', 'Django', 'PostgreSQL', 'AWS']", "posted_date": "2026-05-12"
| # | job_id | title | company_name | location | experience_required | salary_range |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Company Profiles objects from timesjobs.com. All fields typed and schema-versioned.
"company_id": "CMP10482", "name": "TechCorp India", "industry": "IT Software", "employee_count": "1001-5000", "hq_location": "Mumbai", "active_jobs_count": 42, "rating": 4.1, "reviews_count": 312
| # | company_id | name | industry | employee_count | hq_location | website |
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Complete list of extractable fields for Walk-in Details objects from timesjobs.com. All fields typed and schema-versioned.
"walkin_id": "WI49281", "job_title": "Customer Support Executive", "company_name": "Global BPO Services", "interview_date": "2026-05-15", "interview_time": "10:00 AM - 04:00 PM", "venue_address": "Sector 62, Noida, UP", "contact_person": "Rahul Sharma", "location": "Noida"
| # | walkin_id | job_title | company_name | interview_date | interview_time | venue_address |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Salary Insights objects from timesjobs.com. All fields typed and schema-versioned.
"job_role": "Data Engineer", "company_name": "DataFlirt", "min_salary": 1200000, "max_salary": 2200000, "avg_salary": 1650000, "currency": "INR", "experience_level": "3-5 yrs", "location": "Bengaluru"
| # | job_role | company_name | min_salary | max_salary | avg_salary | currency |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Recruiter Data objects from timesjobs.com. All fields typed and schema-versioned.
"recruiter_id": "REC55921", "name": "Priya Desai", "designation": "Talent Acquisition Lead", "company_name": "FinTech Solutions", "active_jobs": 14, "location": "Pune", "contact_status": "Hidden", "last_active": "2026-05-11"
| # | recruiter_id | name | designation | company_name | active_jobs | location |
|---|---|---|---|---|---|---|
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Our TimesJobs scraper handles complex search filters, dynamic pagination, and unstructured text blocks, delivering clean, normalised data for analytics and recruitment intelligence.
Extract title, company, location, experience, salary brackets, required skills, and full descriptions for every active listing.
Capture company profiles, employee counts, industry classifications, and active job volumes across different locations.
Monitor walk-in schedules, venue details, and contact persons for high-volume hiring sectors like BPO and retail.
Parse unstructured salary strings into clean minimum, maximum, and average integer fields for direct database ingestion.
Extract and standardise mandatory and preferred skill tags from job descriptions for accurate market demand analysis.
Normalise multi-city job postings into distinct records, ensuring accurate geographic distribution analysis.
Traverse thousands of result pages reliably, bypassing display limits using targeted search parameters and date filters.
Extract active recruiter profiles, their current hiring volume, and associated companies to build targeted lead lists.
Receive only new, modified, or closed job postings in subsequent runs, reducing your data processing overhead.
Brief in. Clean data out.
Provide search queries, industry filters, or company lists. We design the extraction schema together.
We configure Scrapy crawlers, proxy rotation, and session management for timesjobs.com.
Schema validation, null-rate checks, and data normalisation tests before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Job portals deploy aggressive rate limiting. Here is how we maintain steady extraction rates and ensure schema reliability.
TimesJobs monitors request velocity and IP reputation. Our crawlers use residential ISP proxies from Indian pools, pacing requests to mimic normal user browsing patterns and prevent subnet bans.
Many search filters and pagination controls on TimesJobs rely on client-side state. We execute Playwright sessions to interact with dynamic elements, ensuring comprehensive data capture.
Job descriptions are often poorly formatted HTML. We use a combination of XPath and regex to reliably extract skills, experience, and salary strings regardless of the employer's formatting choices.
We maintain a hash index of active job IDs. When a job disappears from search results, we flag it as closed, giving you accurate time-to-fill metrics without re-scraping the entire platform.
Every run emits structured logs. We alert on null-rate spikes in critical fields like salary or skills, adjusting selectors before they impact your downstream analytics.
Economic researchers and consultancies analyse hiring volumes and skill demands across Indian IT and BPO sectors.
HR teams track competitor hiring velocity, location expansion, and salary brackets to adjust their own recruitment strategies.
Recruitment agencies identify companies with high volumes of open roles to pitch their staffing services.
Compensation platforms ingest salary ranges to build accurate, real-time benchmarks for specific roles and cities.
Educational platforms analyse trending skills in job descriptions to align their course offerings with market demand.
Private equity firms monitor hiring trends as a proxy for company growth and operational health before acquisitions.
"TimesJobs holds critical data on the Indian IT and BPO sectors, but extracting normalised skills and salaries requires a dedicated infrastructure layer."
Most engineering teams underestimate the complexity of scraping job boards. Variations in salary formats, unstructured skill tags, and aggressive bot mitigation lead to broken pipelines. DataFlirt absorbs that complexity so your team can focus on analytics, not infrastructure maintenance.
Everything supported by our timesjobs.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 dynamic filter interaction. Combined for optimal extraction speed.
We maintain pools of residential ISP proxies across Indian regions. Rotation happens per-request to prevent subnet bans and ensure continuous access.
Pipelines run on Kubernetes clusters. 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 timesjobs.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available job postings and company profiles is generally permissible. DataFlirt targets only public, non-authenticated data. We do not extract candidate resumes or bypass employer login walls. Clients should review TimesJobs ToS and consult legal counsel for specific use cases.
We use Indian residential proxies and pace our requests to match normal browsing behaviour. If a CAPTCHA or block is encountered, our system automatically rotates the IP and retries the request.
Yes. We can configure the pipeline to target specific geographic filters or normalise multi-city postings to isolate data for your target locations.
We can configure pipelines to run daily or hourly depending on your requirements. Daily delta feeds ensure you receive new postings within 24 hours of publication.
Yes. Job boards often display salaries in inconsistent formats. We use regex and custom parsers to convert these into clean minimum, maximum, and average integer fields.
Yes. By maintaining state of active job IDs, we can detect when a listing is removed from search results and flag it as closed in your dataset.
Our minimum engagement typically covers a defined set of search queries or categories with weekly or daily delivery. Contact us with your specific scope for a detailed quote.
Absolutely. We provide a sample run of up to 500 job postings based on your criteria so you can validate our schema and parsing accuracy before committing.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a daily feed of IT jobs in Bengaluru or a historical dump of BPO salaries, we scope, build, and operate the pipeline. Tell us what you need.
