We extract software engineering roles, salary bands, required tech stacks, and company profiles from Dice. 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 dice.com. All fields typed and schema-versioned.
"job_id": "90210444_123456", "title": "Senior Backend Engineer (Python/Go)", "company_name": "CyberTech Solutions", "location": "Austin, TX", "employment_type": "Full-Time", "salary_min": 140000, "salary_max": 175000, "remote_status": "Hybrid", "posted_date": "2026-10-14T08:30:00Z"
| # | job_id | title | company_name | location | employment_type | salary_min |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Skills & Tech Stack objects from dice.com. All fields typed and schema-versioned.
"job_id": "90210444_123456", "primary_skills": "['Python', 'Golang', 'AWS']", "experience_years_required": 5, "education_level": "Bachelor's Degree", "certifications": "['AWS Certified Solutions Architect']", "framework_mentions": "['Django', 'FastAPI']", "cloud_providers": "['AWS', 'GCP']"
| # | job_id | primary_skills | secondary_skills | experience_years_required | education_level | certifications |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Company Profiles objects from dice.com. All fields typed and schema-versioned.
"company_id": "C_88392", "name": "CyberTech Solutions", "industry": "Cybersecurity", "size": "501-1000", "headquarters": "Austin, TX", "active_jobs_count": 42, "rating": 4.1, "website": "https://cybertech.example.com"
| # | company_id | name | industry | size | website | headquarters |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Salary Data objects from dice.com. All fields typed and schema-versioned.
"job_id": "90210444_123456", "title": "Senior Backend Engineer", "provided_salary_min": 140000, "provided_salary_max": 175000, "dice_estimated_min": 135000, "dice_estimated_max": 180000, "pay_period": "Yearly", "currency": "USD"
| # | job_id | title | location | provided_salary_min | provided_salary_max | dice_estimated_min |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Recruiter & Agency objects from dice.com. All fields typed and schema-versioned.
"job_id": "90210444_123456", "agency_name": "TechTalent Partners", "agency_id": "A_9921", "recruiter_name": "Sarah Jenkins", "active_listings": 115, "posting_frequency": "Daily", "contact_info": "sarah.j@techtalent.example.com"
| # | job_id | recruiter_name | agency_name | agency_id | contact_info | posting_frequency |
|---|---|---|---|---|---|---|
| 1 | ||||||
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| 3 |
Our Dice scraper handles the complexities of job board extraction: parsing unstructured descriptions, standardising tech stacks, identifying agency listings, and capturing accurate salary bands.
Extract raw HTML or parsed markdown for every tech role to feed your internal NLP pipelines.
Capture stated compensation, hourly rates, and Dice estimated salary ranges, normalised to a standard currency and pay period.
Extract and map required skills, frameworks, and programming languages from unstructured job text.
Identify exact work models, timezone requirements, and relocation packages associated with the role.
Track DoD, TS/SCI, and Public Trust clearance requirements for defense and government contracting roles.
Distinguish direct employer postings from staffing agency requisitions to calculate true market demand.
Parse employment types, contract durations, and C2C/W2 eligibility criteria.
Query via Dice's proprietary semantic search parameters and filters to target exact market segments.
Track new roles, closed positions, and modified listings daily without processing full re-crawls.
Brief in. Clean data out.
Provide target job titles, locations, skills, or company IDs. We design the extraction schema together.
We configure Scrapy crawlers, proxy rotation, session management, and CAPTCHA handling for dice.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 protect their listings aggressively. Here is how we maintain pipeline stability and data quality.
Dice uses strict WAFs and rate limiting. We route requests through US-based residential proxies with realistic browser fingerprints and randomised request timing to prevent IP bans.
Dice's search results load dynamically via API calls. We intercept the backend XHR requests directly, bypassing frontend rendering overhead and extracting raw JSON payloads.
Job descriptions vary wildly between agencies. We use NLP-backed fallback parsers to extract skills and salary data consistently, regardless of how the recruiter formatted the text.
We track job ID hashes to only emit newly posted, modified, or closed roles. This reduces compute cost and downstream processing load for continuous intelligence feeds.
We alert on total job count drops, location parsing failures, and schema drift. If Dice changes their API structure, our engineers are notified before your next delivery window.
Track demand for specific tech stacks, cloud certifications, and AI skills across different geographic regions.
HR teams analyse compensation bands for software engineers across different US tech hubs to remain competitive.
Identify companies actively scaling their engineering teams to pitch developer tools and enterprise software.
Monitor which tech recruiters are winning requisitions and track their hiring volume over time.
Quantify the shift between fully remote, hybrid, and RTO mandates in the technology sector.
Analyse competitor job descriptions and benefit offerings to optimise hiring pipelines and improve application rates.
"Dice holds the highest-density dataset for US technology hiring and compensation — but extracting structured skill requirements from free-text descriptions requires dedicated parsing infrastructure."
Scraping tech job boards involves parsing unstructured text, managing API rate limits, and bypassing strict anti-bot measures. DataFlirt handles the extraction, normalisation, and diffing logic so your data science teams receive clean, queryable tech stacks and salary bands — without maintaining the pipeline.
Everything supported by our dice.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, deduplication, and retry logic. Playwright handles JavaScript rendering, cookie sessions, and interaction flows. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies across US regions. Rotation happens per-request with sticky sessions where required. IP score monitoring prevents blacklisted pool contamination.
Pipelines run on AWS Lambda (burst) and ECS (sustained). Airflow handles scheduling, dependency management, and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About dice.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available job postings from Dice is generally permissible. DataFlirt targets only public, non-authenticated job and company data. We do not extract personal candidate data or circumvent authentication walls.
We use US residential ISP proxies, headless browser sessions with realistic fingerprints, and request timing modelled on human behaviour. We also target backend APIs directly where possible to minimise WAF triggers.
Yes. We use custom parsing rules to extract and normalise programming languages, frameworks, cloud platforms, and certifications from unstructured job descriptions.
Pipelines can be configured to run daily or at custom intervals. We use change detection to output only new or modified jobs, ensuring your warehouse is always up to date.
Yes. We capture company profile data and agency identifiers to differentiate direct employer requisitions from staffing firm postings.
Absolutely. We provide a sample run based on your specific search criteria as part of the pre-engagement scoping process so you can validate schema fit and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off export of remote Python roles or a continuous feed of US tech hiring data — we scope, build, and operate the pipeline. Tell us what you need.
