We extract hyperlocal job postings, salary bands, skill prerequisites, and employer metadata from apna.co. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your schedule.
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 apna.co. All fields typed and schema-versioned.
"job_id": "APN-982374", "title": "Delivery Executive", "company": "Zepto", "location_city": "Bengaluru", "location_locality": "Koramangala", "salary_min": 25000, "salary_max": 35000, "job_type": "Full Time", "experience_req": "Fresher"
| # | job_id | title | company | location_city | location_locality | salary_min |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Salary & Compensation objects from apna.co. All fields typed and schema-versioned.
"job_id": "APN-982374", "base_salary_min": 25000, "base_salary_max": 30000, "incentives_available": true, "pf_esic_benefits": true, "travel_allowance": 2000, "salary_frequency": "Monthly", "currency": "INR"
| # | job_id | title | base_salary_min | base_salary_max | incentives_available | pf_esic_benefits |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Employer Metadata objects from apna.co. All fields typed and schema-versioned.
"company_id": "CMP-44512", "company_name": "Zepto", "industry": "Logistics & Delivery", "active_jobs_count": 342, "verified_status": true, "hr_name_public": "Rahul S.", "employer_rating": 4.2
| # | company_id | company_name | industry | company_size | headquarters | hr_name_public |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Interview Details objects from apna.co. All fields typed and schema-versioned.
"job_id": "APN-982374", "interview_type": "Walk-In", "walk_in_address": "Sector 4, HSR Layout, Bengaluru", "hr_contact_visible": false, "contact_timing": "10:00 AM to 5:00 PM", "required_documents": "['Aadhar Card', 'Driving Licence', 'PAN Card']", "hiring_urgency": "High"
| # | job_id | interview_type | walk_in_address | hr_contact_visible | contact_timing | required_documents |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Skill Requirements objects from apna.co. All fields typed and schema-versioned.
"job_id": "APN-982374", "primary_skills": "['Two Wheeler Driving', 'Navigation']", "education_level": "10th Pass", "vehicle_req": "Two Wheeler", "smartphone_req": true, "gender_preference": "Any", "age_limit": "18 to 35"
| # | job_id | primary_skills | secondary_skills | education_level | certification_req | vehicle_req |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Apna scraper parses hyperlocal job boards, salary ranges, and skill prerequisites with full JavaScript rendering and regional proxy routing built in.
Extract job data down to the city, locality, and ward level for precise geographic mapping of labor demand.
Capture minimum and maximum salary ranges, along with incentive structures, PF, and ESIC benefit flags.
Track specific prerequisites like two-wheeler ownership, smartphone requirements, education levels, and language proficiency.
Identify trusted employers versus unverified accounts to maintain data quality in your downstream analytics.
Extract walk-in addresses, HR contact availability windows, and specific document requirements for candidates.
Categorise postings by night shift, rotational, part-time, full-time, and gig work models.
Capture job descriptions and titles published in local scripts to ensure no data is lost in translation.
Identify new postings and detect expired or filled roles using hash-based diffing across daily runs.
Configure continuous pipelines at daily or weekly cadences to keep your warehouse synchronised with the live platform.
Brief in. Clean data out.
Provide target cities, job categories, or specific employer lists. We design the extraction schema together.
We configure Scrapy crawlers, Indian residential proxies, session management, and API interception for apna.co.
Schema validation, null-rate checks, and geolocation accuracy verification before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Job boards deploy aggressive rate limiting to protect their inventory. Here is how we maintain pipeline stability.
Apna serves highly localised content based on the requester IP. We route traffic through specific Indian city proxies to view accurate job listings for Bengaluru, Delhi, Mumbai, and tier-2 cities.
Modern job boards use single-page application architectures. We use Playwright to execute JavaScript, trigger lazy-loading, and hydrate full job lists that simple HTTP clients miss.
Instead of relying purely on fragile DOM parsing, our pipeline intercepts the underlying GraphQL and REST API calls made by the Apna frontend, capturing cleaner and more comprehensive JSON payloads.
Platform updates frequently alter job card layouts. We employ multiple fallback chains per field, combining JSON API extraction with CSS and XPath selectors to ensure pipeline continuity.
Employers often post identical jobs across multiple localities. We generate cryptographic hashes based on job descriptions, employer IDs, and coordinates to flag duplicates and maintain dataset integrity.
HR teams and recruiters track local pay scales and incentive structures to remain competitive in high-attrition blue-collar sectors.
Economists and researchers analyse hiring volumes, skill demands, and geographic shifts in the informal and rising workforce economy.
B2B service providers target actively hiring companies to pitch HR software, background verification services, and staffing solutions.
EdTech and vocational training firms identify frequently requested skills and certifications to design relevant curriculum for job seekers.
Investors track job posting velocity by city tier and locality as a proxy for regional economic health and consumer demand.
Logistics and quick-commerce companies monitor delivery partner demand and compensation across competing platforms.
"Apna maps the informal and blue-collar economy of India at a granular level, but accessing this data at scale requires specialised infrastructure."
Most engineering teams fail at extracting job board data because they rely on simple HTTP clients and datacenter IPs. Reliable Apna scraping requires Indian residential proxies, dynamic JavaScript execution, and daily schema monitoring. DataFlirt manages the infrastructure so your team can query the data directly.
Everything supported by our apna.co 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 manages JavaScript execution and dynamic content hydration for single-page application architectures.
We maintain pools of Indian residential proxies to bypass regional blocks and rate limits. IP score monitoring prevents blacklisted pool contamination.
Pipelines run on Kubernetes for sustained loads. Airflow handles scheduling, dependency management, and SLA alerting, with all state stored in PostgreSQL.
Data delivered to where your team already works — no new tooling required.
About apna.co scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available job postings is generally permissible for non-destructive use cases. DataFlirt targets only public, non-authenticated job and employer metadata. We do not extract candidate PII, circumvent authentication walls, or violate data protection regulations. Clients should review platform terms of service for specific use cases.
We use Indian residential proxies, randomised request timing, and API interception to minimise the footprint on the target servers. If a block occurs, the system automatically rotates the proxy and retries the request.
Yes. The pipeline can be configured to target specific geographic parameters, extracting jobs only from designated cities, pin codes, or localities as defined in your scope.
Pipelines typically run on a daily cadence, ensuring that expired jobs are flagged and new postings are captured within 24 hours of publication.
Yes. When the platform provides granular compensation structures, we separate base salary ranges from potential incentives, travel allowances, and statutory benefits.
Absolutely. We provide a sample run of up to 500 job postings for your target region so you can validate schema fit and data quality before signing a contract.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a daily feed of delivery jobs in Bengaluru or national salary benchmarks across 50 cities, we build and operate the pipeline. Tell us what you need.
