We extract sitter profiles, boarding rates, walking schedules, repeat client scores, and reviews from Rover. 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 Sitter Profiles objects from rover.com. All fields typed and schema-versioned.
"sitter_id": "RVR-84729", "name": "Sarah M.", "location_zip": "90210", "repeat_clients": 42, "response_rate": 100, "response_time_minutes": 15, "star_rating": 4.9, "background_checked": true
| # | sitter_id | name | location_zip | headline | about_text | repeat_clients |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Services objects from rover.com. All fields typed and schema-versioned.
"sitter_id": "RVR-84729", "boarding_rate": 45.0, "walking_rate": 20.0, "holiday_markup": 15.0, "puppy_rate": 55.0, "cancellation_policy": "Moderate", "currency": "USD"
| # | sitter_id | boarding_rate | house_sitting_rate | drop_in_rate | walking_rate | doggy_day_care |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reviews objects from rover.com. All fields typed and schema-versioned.
"review_id": "REV-992834", "sitter_id": "RVR-84729", "owner_name": "James L.", "rating": 5, "review_date": "2026-03-14", "verified_stay": true, "dog_name": "Max"
| # | review_id | sitter_id | owner_name | review_date | rating | review_text |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Availability Calendar objects from rover.com. All fields typed and schema-versioned.
"sitter_id": "RVR-84729", "date": "2026-11-24", "is_available": false, "slots_remaining": 0, "service_type": "boarding", "blackout_date": true, "updated_at": "2026-05-12T09:14:00Z"
| # | sitter_id | date | is_available | slots_remaining | service_type | blackout_date |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Search Results objects from rover.com. All fields typed and schema-versioned.
"search_zip": "90210", "service_filter": "dog_walking", "rank_position": 3, "sitter_id": "RVR-84729", "base_price": 20.0, "distance_miles": 2.4, "promoted_badge": false
| # | search_zip | service_filter | dates_filter | rank_position | sitter_id | name |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Rover scraper extracts deep profile metrics, dynamic pricing grids, and live availability calendars. We handle geographic search rendering and anti-bot systems automatically.
Extract names, headlines, bios, repeat client counts, response metrics, and background check verification badges.
Capture base rates for boarding, walking, and day care, plus granular markups for holidays, puppies, and extra pets.
Scrape forward-looking calendar data to identify blackout dates, remaining capacity, and seasonal supply constraints.
Pull complete review text, star ratings, verified stay flags, and sitter responses across all paginated review tabs.
Execute searches across thousands of postcodes to map supply density and rank positions for specific service types.
Track Rover Go status, Star Sitter designations, and top-ranked placement indicators to measure platform preference.
Log cancellation policies and house rules to analyse flexibility trends among top-performing sitters.
Run continuous pipelines that only emit records when a sitter changes their rates, updates their calendar, or receives a new review.
Scrape Rover data across US, UK, Canada, and European markets with native currency normalisation.
Brief in. Clean data out.
Provide postcodes, service types, or specific sitter URLs. We design the extraction schema together.
We configure Scrapy crawlers, proxy rotation, session management, and geographic targeting for rover.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.
Rover uses location-based rendering and dynamic calendar hydration. Here is how we maintain stable extraction.
Rover search results depend heavily on the searcher's location. We inject specific GPS coordinates and postcode parameters into the browser session to ensure accurate local search rankings.
Availability calendars are not present in the static HTML. We use Playwright to execute the client-side JavaScript, interact with the calendar widget, and extract forward-looking availability state.
High-density areas cap search pagination. We subdivide large search radii into overlapping micro-grids to ensure 100% extraction coverage without hitting platform display limits.
We route requests through residential ISP proxies matching the target search region, preventing IP bans and ensuring the platform returns realistic local pricing data.
Rover frequently updates its profile layouts. We use multi-layer fallback chains targeting JSON-LD objects and internal API endpoints to maintain pipeline stability during frontend changes.
Pet care startups track local boarding and walking rates to optimise their own pricing models across different cities.
Analysts correlate sitter availability calendars with holiday seasons to predict market capacity constraints.
Rival platforms monitor top-performing sitters, review velocity, and repeat client metrics to identify acquisition targets.
Machine learning teams use structured review text and rating data to train sentiment analysis models for the gig economy.
Private equity firms track active sitter counts and service density to evaluate platform growth in specific geographic markets.
Researchers extract background check statuses and policy enforcement data to study platform safety standards.
"Rover holds the definitive dataset on local pet care pricing and supply density. Accessing it requires navigating complex geographic rendering."
Extracting data from Rover requires precise geographic spoofing, JavaScript execution for calendar state, and residential proxies to avoid rate limits. DataFlirt manages this entire infrastructure, delivering clean market intelligence directly to your warehouse.
Everything supported by our rover.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 manages JavaScript execution for calendar widgets and location spoofing.
We maintain pools of residential ISP proxies. Rotation happens per-request with sticky sessions to simulate genuine local search traffic.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling and dependency management. All state is stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About rover.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Rover is generally permissible. DataFlirt targets only public sitter profiles, rates, and reviews. We do not extract personal owner data or private messages. Clients should review Rover terms of service and consult legal counsel.
We use Playwright to inject precise GPS coordinates and postcode parameters into the browser session, ensuring the search results perfectly match the local market conditions you want to monitor.
Yes. We execute the calendar JavaScript to extract availability status, blackout dates, and remaining capacity for specific service types up to several months in advance.
Pipelines can run at daily or weekly cadences depending on your requirements. Change detection ensures you only process updates when a sitter modifies their rates.
We paginate through the entire review history for each sitter profile, extracting text, ratings, dates, and verified stay indicators.
Our smallest packages start at tracking specific postcodes or a defined list of sitter URLs. Contact us with your target regions for a scoped quote.
Yes. We provide a sample run of up to 500 sitter profiles in a designated postcode to 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 national pricing audit or continuous availability tracking in specific postcodes, we build and operate the pipeline. Tell us what you need.
