We extract pet caregiver profiles, service rates, user reviews, and geographic coverage from Wag!. 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 Caregiver Profiles objects from wagwalking.com. All fields typed and schema-versioned.
"caregiver_id": "WAG-CG-847291", "first_name": "Sarah", "last_initial": "M.", "headline": "Experienced dog walker and sitter", "rating": 4.9, "review_count": 342, "walks_completed": 856, "background_checked": true
| # | caregiver_id | first_name | last_initial | headline | bio | rating |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Service Pricing objects from wagwalking.com. All fields typed and schema-versioned.
"caregiver_id": "WAG-CG-847291", "service_type": "Dog Walking", "duration_minutes": 30, "base_price": 25.0, "additional_pet_fee": 5.0, "holiday_rate": 35.0, "currency": "USD"
| # | caregiver_id | service_type | duration_minutes | base_price | additional_pet_fee | holiday_rate |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reviews & Ratings objects from wagwalking.com. All fields typed and schema-versioned.
"review_id": "REV-9938210", "caregiver_id": "WAG-CG-847291", "star_rating": 5, "review_text": "Sarah is amazing with our golden retriever!", "review_date": "2023-10-14", "pet_name": "Buddy", "service_type": "Dog Walking"
| # | review_id | caregiver_id | reviewer_name | star_rating | review_text | review_date |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Geographic Coverage objects from wagwalking.com. All fields typed and schema-versioned.
"caregiver_id": "WAG-CG-847291", "city": "Austin", "state": "TX", "zip_code": "78704", "service_radius_miles": 10, "coordinates_lat": 30.2456, "coordinates_lon": -97.7688
| # | caregiver_id | city | state | zip_code | service_radius_miles | coordinates_lat |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Search Results objects from wagwalking.com. All fields typed and schema-versioned.
"search_location": "78704", "service_filter": "Dog Walking", "position": 3, "caregiver_id": "WAG-CG-847291", "rating": 4.9, "starting_price": 25.0, "scraped_at": "2023-11-01T14:22:10Z"
| # | search_location | service_filter | position | caregiver_id | name | headline |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Wag! scraper handles location-based search grids, dynamic pricing matrices, and paginated review corpora — bypassing bot protection to deliver structured data.
Extract bio, completed walk counts, background check verification, response rates, and acquired badges for every caregiver.
Capture granular rates for walking, sitting, boarding, and drop-ins, including duration multipliers and additional pet fees.
Paginate through historical reviews to extract star ratings, text, date, and associated pet types for sentiment analysis.
Simulate searches across thousands of ZIP codes to map caregiver density, service availability, and regional pricing variations.
Monitor calendar availability signals to estimate caregiver utilisation rates and platform supply elasticity.
Track organic search ranking for caregivers across specific geographic queries and service filters.
Extract background check status, platform endorsements, and recurring client metrics.
Run continuous pipelines that output only changed records — tracking price adjustments and new reviews over time.
All extracted fields are typed, cleaned, and mapped to a consistent schema before warehouse insertion.
Brief in. Clean data out.
Provide target ZIP codes, cities, or specific caregiver IDs. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for wagwalking.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.
Extracting location-based gig economy data requires sophisticated geographic targeting and bot mitigation. Here is how we handle it.
Wag! serves results based on geographic proximity. We route requests through state- and city-level residential proxies to accurately capture local search rankings and pricing tiers without triggering location-mismatch flags.
Search maps, calendar availability, and paginated reviews rely heavily on client-side rendering. We use Playwright to execute JavaScript, trigger lazy-loaded elements, and intercept XHR payloads directly.
We bypass automated scraping defenses by rotating TLS fingerprints, maintaining realistic HTTP headers, and injecting human-like interaction patterns during browser automation sessions.
Extracting deep profile data requires maintaining session state across multiple page navigations. Our infrastructure handles cookie persistence and token refresh cycles automatically.
Gig economy pricing can have extreme outliers. We implement strict schema validation and statistical bounds checking to flag anomalous rates before they reach your warehouse.
Pet care startups and local agencies monitor Wag! rates across different ZIP codes to optimise their own pricing strategies.
Marketplaces analyse caregiver density and review velocity to identify underserved geographic regions for expansion.
Analysts and researchers track workforce participation, earnings potential, and platform retention metrics.
Private equity firms monitor platform supply growth, review volume, and geographic footprint to evaluate market share.
NLP teams utilise structured review corpora to train sentiment analysis models specific to the pet care industry.
B2B pet product companies identify highly rated, professional pet caregivers for targeted partnership outreach.
"Geospatial marketplace data reveals the exact supply-and-demand dynamics of the gig economy — if you can extract it reliably across thousands of coordinates."
Scraping location-dependent platforms like Wag! requires precise proxy targeting and deep JavaScript execution. DataFlirt manages the proxy pools, session state, and schema normalisation, delivering clean tabular data so your team can focus on geospatial analysis rather than pipeline maintenance.
Everything supported by our wagwalking.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 wagwalking.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information is generally permissible under applicable law. DataFlirt targets only public, non-authenticated caregiver profiles, pricing, and reviews. We do not extract personal client data, circumvent authentication walls, or scrape private messages. Clients should review Wag!'s ToS and consult legal counsel for specific use cases.
We use city- and state-level residential proxies to simulate searches from specific locations. This ensures we capture accurate local rankings, availability, and regional pricing differences without triggering location-mismatch defenses.
Yes. We paginate through the entire review history for specified caregivers, extracting star ratings, text, dates, and the type of service provided.
Pipelines can be configured to run daily, weekly, or monthly depending on your requirements. We use change-detection diffing to track pricing adjustments over time.
Yes. We capture platform-verified signals including background check status, completion metrics, and specific caregiver badges displayed on public profiles.
Our smallest packages start at a defined list of target ZIP codes or cities with weekly delivery. For national coverage or custom schema requirements, we price based on volume and delivery frequency.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a national pricing audit or continuous caregiver tracking across target cities — we scope, build, and operate the pipeline. Tell us what you need.
