We extract developer profiles, design portfolios, skill matrices, and hiring guides from Toptal. Delivered as clean JSON, CSV, or Parquet to S3 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 Freelancer Profiles objects from toptal.com. All fields typed and schema-versioned.
"profile_id": "dev-9821", "category": "Developer", "primary_title": "Senior Python Engineer", "years_experience": 8, "location": "Berlin, Germany", "timezone": "UTC+1", "availability": "Full-time", "core_skills": "['Python', 'Django', 'PostgreSQL']"
| # | profile_id | category | primary_title | years_experience | location | timezone |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Portfolio Items objects from toptal.com. All fields typed and schema-versioned.
"portfolio_id": "port-441", "profile_id": "dev-9821", "project_title": "Fintech Payment Gateway", "role": "Lead Backend Developer", "industry": "Financial Services", "technologies_used": "['Python', 'FastAPI', 'Redis']"
| # | portfolio_id | profile_id | project_title | description | role | industry |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Skill Directories objects from toptal.com. All fields typed and schema-versioned.
"skill_id": "sk-py-01", "skill_name": "Python", "category": "Development", "total_experts": 4192, "average_rate_estimate": "$80-$120/hr", "top_locations": "['United States', 'United Kingdom', 'Germany']"
| # | skill_id | skill_name | category | related_skills | total_experts | average_rate_estimate |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Work Experience objects from toptal.com. All fields typed and schema-versioned.
"experience_id": "exp-881", "profile_id": "dev-9821", "company_name": "Stripe", "title": "Backend Engineer", "start_date": "2019-03-01", "is_current": false, "technologies": "['Ruby', 'Go']"
| # | experience_id | profile_id | company_name | title | start_date | end_date |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Hiring Guides objects from toptal.com. All fields typed and schema-versioned.
"guide_id": "hg-python", "topic": "How to Hire a Python Developer", "author": "Toptal Engineering Team", "publish_date": "2023-11-12", "interview_questions": "['Explain the GIL', 'How do decorators work?']", "cost_benchmark": "$70-$150/hr"
| # | guide_id | topic | author | publish_date | interview_questions | required_skills |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Toptal scraper handles complex React hydration, strict rate limits, and nested profile directories to deliver clean, structured talent data.
Extract anonymised talent profiles, titles, bios, and skill matrices across developer, designer, and finance categories.
Capture project descriptions, roles, and tech stacks from designer and developer portfolios.
Extract Toptal's hierarchical skill directory and related technology tags to map talent density.
Parse chronological work history and academic backgrounds for talent density analysis.
Scrape interview questions, evaluation rubrics, and hiring benchmarks published by Toptal.
Execute React-based dynamic content loading for infinite-scroll profile directories.
Capture timezone, availability status, and regional rate indicators where public.
Bypass Cloudflare and strict rate limits using residential proxy rotation.
Monitor talent directories for new additions or skill updates with hash-based diffing.
Brief in. Clean data out.
Provide skill categories or target URLs. We design the extraction schema together.
We configure Scrapy crawlers, proxy rotation, and session management for toptal.com.
Schema validation, null-rate checks, and sample profile reviews before full launch.
JSON / CSV / Parquet pushed to your S3 bucket or Snowflake stage on agreed cadence.
Toptal uses aggressive edge protection and dynamic content delivery. Here is how we maintain reliable extraction.
Toptal uses aggressive edge protection. We route requests through residential IPs with TLS fingerprint spoofing to maintain access and avoid IP bans.
Profile data is often loaded via background XHR requests. We intercept these API calls directly or render the full DOM via Playwright to ensure complete data capture.
Developer, designer, and finance profiles use different DOM structures. We maintain distinct fallback chains for each category to prevent schema breakage.
Skill directories use infinite scroll or complex pagination. Our crawlers manage state to extract every node without duplication or missing records.
We maintain a hash index of last-seen profiles. Subsequent runs only push diffs, reducing storage bloat and downstream processing load for your team.
Identify top-tier skill profiles, map talent density by region, and build proprietary sourcing databases.
Analyse hourly rate indicators and availability across different technology stacks and geographies.
Track the growth of emerging technologies by monitoring the volume of experts adding new skills to their profiles.
Consultancies monitor Toptal's talent pool depth and hiring guides to benchmark their own vetting processes.
Extract technical interview questions and hiring guides to standardise internal engineering assessments.
Train HR matching algorithms and resume parsers using highly structured, verified professional profiles and portfolios.
"Toptal represents the top 3 percent of freelance talent globally. Extracting this dataset provides an unparalleled benchmark for elite engineering and design skills."
Scraping Toptal requires bypassing strict edge protection and handling highly dynamic, React-rendered profile structures. Our managed pipelines handle the proxy rotation, API interception, and schema normalization so your data science teams receive clean, structured talent data ready for immediate analysis.
Everything supported by our toptal.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 React hydration for complex profile views.
We maintain pools of residential ISP proxies to bypass edge protection and IP bans. Rotation happens per request with sticky sessions.
Pipelines run on AWS Lambda and Kubernetes. 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 toptal.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available professional profiles and skill directories is generally permissible under applicable law. DataFlirt targets only public, non-authenticated data. We do not extract private contact details or bypass authentication walls.
We use residential ISP proxies and realistic TLS fingerprints to bypass Cloudflare and edge security. Request timing is randomised to mimic human browsing behaviour.
No. Toptal restricts emails and phone numbers to authenticated clients. We only extract publicly visible profile information, bios, and work history.
Yes. We extract the CDN URLs for images and case study assets associated with designer and developer portfolios.
We support daily, weekly, or monthly refresh cycles. Our change detection system ensures you only process updated or newly added profiles.
Yes. We provide a sample run of up to 500 profiles or skill nodes as part of the pre-engagement scoping process to validate schema fit and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off skill directory dump or continuous profile monitoring, we scope, build, and operate the pipeline. Tell us what you need.
