We extract tutor profiles, hourly rates, subject expertise, reviews, and availability from Superprof. 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 Tutor Profiles objects from superprof.com. All fields typed and schema-versioned.
"tutor_id": "SP-849201", "name": "Sarah J.", "headline": "Experienced Mathematics Tutor for A-Levels and GCSEs", "location": "London, UK", "is_online": true, "super_tutor": true, "response_time": "1 hour", "response_rate": 100
| # | tutor_id | name | headline | bio | location | is_online |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing Data objects from superprof.com. All fields typed and schema-versioned.
"tutor_id": "SP-849201", "base_rate": 35.0, "currency": "GBP", "first_lesson_free": true, "pack_5_discount": 160.0, "pack_10_discount": 300.0, "webcam_fee": 0.0, "travel_fee": 5.0
| # | tutor_id | base_rate | currency | first_lesson_free | pack_5_discount | pack_10_discount |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reviews & Ratings objects from superprof.com. All fields typed and schema-versioned.
"review_id": "REV-99281", "tutor_id": "SP-849201", "reviewer_name": "James T.", "rating": 5.0, "review_text": "Sarah helped me jump two grades in my mock exams.", "date_posted": "2026-03-14", "subject": "Mathematics", "status": "published"
| # | review_id | tutor_id | reviewer_name | rating | review_text | date_posted |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Search Results objects from superprof.com. All fields typed and schema-versioned.
"keyword": "mathematics", "location_query": "London", "rank_position": 3, "tutor_id": "SP-849201", "name": "Sarah J.", "hourly_rate": 35.0, "rating": 5.0, "review_count": 42
| # | keyword | location_query | rank_position | tutor_id | name | headline |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Subject Expertise objects from superprof.com. All fields typed and schema-versioned.
"tutor_id": "SP-849201", "primary_subject": "Mathematics", "secondary_subjects": "['Physics', 'Further Maths']", "levels": "['GCSE', 'A-Level', 'University']", "methodology": "I focus on exam technique and past papers.", "languages_spoken": "['English', 'French']", "background": "BSc Mathematics, UCL"
| # | tutor_id | primary_subject | secondary_subjects | levels | methodology | background |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Superprof scraper handles every layer of the platform: tutor profiles, dynamic pricing, subject lists, and the review corpus : with JavaScript rendering, session management, and anti-bot circumvention built in.
Extract names, headlines, bios, verified badges, Super Tutor status, and membership dates for every tutor on the platform.
Capture hourly rates, currencies, bulk lesson discounts, travel fees, and First Lesson Free status for exact price intelligence.
Extract primary subjects, secondary subjects, teaching levels, and detailed methodology text from every profile.
Scrape full review text, star ratings, reviewer names, and dates to build a complete picture of tutor quality.
Track organic position for any subject and location combination to monitor visibility and market saturation.
Extract data from superprof.co.uk, superprof.fr, superprof.in, and 30 other localised domains from a unified schema.
Capture average response times and response rates to gauge tutor activity levels and platform engagement.
Extract stated locations, online teaching availability, and travel willingness for local market analysis.
Run one-off bulk exports or configure continuous pipelines at weekly or monthly cadences with change-detection diffing.
Brief in. Clean data out.
Provide subject lists, location parameters, or tutor IDs. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for superprof.com.
Schema validation, null-rate checks, price-outlier detection, and sample reviews before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Superprof limits pagination and blocks high-volume traffic. Here is how we stay resilient : and why teams choose managed infrastructure over DIY.
Superprof restricts high-frequency requests from data centre IPs. Our crawlers use residential ISP proxies with realistic browser fingerprints, randomised request timing, and full cookie session management to blend in with normal student traffic.
Many interface elements, including booking calendars and full review lists, rely on JavaScript. We run full Playwright browser sessions to trigger lazy-loads and capture data that basic HTTP clients miss.
Superprof tailors search results based on the IP location of the user. We route requests through region-specific proxies to ensure we capture accurate local rankings and currency data.
Platform layouts change without warning. Our selector strategy uses multiple fallback chains per field so a minor layout update does not break your data pipeline overnight.
For large tutor catalogues, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs, reducing compute cost and downstream processing load.
Online tutoring platforms track Superprof supply metrics, subject popularity, and tutor acquisition trends to benchmark their own growth.
Marketplaces monitor hourly rates across subjects and cities to optimise their own pricing models and commission structures.
Agency recruiters identify highly rated, responsive tutors in specific academic niches for direct outreach and onboarding.
Researchers analyse the gig economy, private education costs, and geographical disparities in tutoring availability.
Businesses correlate review velocity and subject density with local educational demand to launch targeted marketing campaigns.
Course aggregators build comprehensive directories of local learning opportunities by combining Superprof data with other sources.
"Superprof holds the largest global dataset of independent tutoring rates and subject demand, but extracting cross-border pricing signals requires dedicated infrastructure."
Most teams underestimate the complexity of scraping global marketplaces. Extracting accurate pricing requires local IP addresses, automated currency normalisation, and handling complex pagination. DataFlirt manages this infrastructure so your engineers can focus on building products, not fighting CAPTCHAs.
Everything supported by our superprof.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 global regions. Rotation happens per request with sticky sessions where required. IP score monitoring prevents blacklisted pool contamination.
Pipelines run on AWS Lambda and ECS. 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 superprof.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Superprof is generally permissible under applicable law. DataFlirt targets only public, non-authenticated tutor profiles, pricing, and review data. We do not extract personal contact details hidden behind the booking wall or violate GDPR. Clients should review Superprof Terms of Service and consult legal counsel for specific use cases.
We use residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour. We monitor for 403 or CAPTCHA rate spikes in real time and trigger pool rotation automatically.
We support all regional variants including superprof.com, superprof.co.uk, superprof.fr, superprof.in, superprof.es, and others, mapping varied language structures into a single normalised schema.
Pipelines typically run on weekly or monthly schedules depending on client needs. Full platform refreshes complete within a 12 to 24 hour window depending on the target region size.
No. Superprof hides direct contact details like phone numbers and emails behind their paid Student Pass system. We only extract public profile data.
Our smallest packages start at a defined subject or city list with monthly delivery. For global catalogues or custom schema requirements, we price based on volume and delivery frequency.
Absolutely. We provide a sample run of up to 500 tutor profiles or 50 search result pages 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 profile dump or a continuous price-monitoring feed across multiple countries, we scope, build, and operate the pipeline. Tell us what you need.
