We extract restaurant catalogues, menu structures, modifier pricing, delivery estimates, and DashPass eligibility. 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 Storefronts objects from doordash.com. All fields typed and schema-versioned.
"store_id": "248192", "name": "Sweetgreen", "cuisine_tags": "['Healthy', 'Salads', 'Bowls']", "rating": 4.8, "review_count": 3102, "dashpass_eligible": true, "delivery_fee": 1.99, "delivery_time_min": 25
| # | store_id | name | cuisine_tags | rating | review_count | dashpass_eligible |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Menus objects from doordash.com. All fields typed and schema-versioned.
"item_id": "item_99281", "store_id": "248192", "category_name": "Warm Bowls", "item_name": "Harvest Bowl", "base_price": 14.45, "currency": "USD", "is_sold_out": false
| # | menu_id | store_id | category_name | item_id | item_name | description |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Modifiers & Options objects from doordash.com. All fields typed and schema-versioned.
"item_id": "item_99281", "group_name": "Choose your dressing", "min_selections": 1, "max_selections": 2, "option_name": "Balsamic Vinaigrette", "additional_price": 0.0, "is_default": true
| # | item_id | modifier_group_id | group_name | min_selections | max_selections | option_id |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Delivery & Fees objects from doordash.com. All fields typed and schema-versioned.
"store_id": "248192", "geohash": "9q8yy", "delivery_fee": 3.99, "dashpass_delivery_fee": 0.0, "service_fee_pct": 15.0, "min_order_subtotal": 12.0, "surge_pricing_active": false
| # | store_id | delivery_address | geohash | delivery_fee | service_fee_pct | min_order_subtotal |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Promos & Offers objects from doordash.com. All fields typed and schema-versioned.
"store_id": "248192", "offer_id": "promo_551", "title": "Spend $20, Save $5", "discount_type": "FLAT", "discount_value": 5.0, "min_spend": 20.0
| # | store_id | offer_id | title | description | discount_type | discount_value |
|---|---|---|---|---|---|---|
| 1 | ||||||
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| 3 |
Our DoorDash scraper bypasses strict bot protection to extract location-specific menus, nested modifiers, and dynamic delivery fees. We handle coordinate targeting and GraphQL interception natively.
Capture categories, item names, descriptions, base prices, and dietary tags across thousands of storefronts.
Extract complex modifier groups, minimum/maximum selection rules, and add-on pricing for highly customisable items.
Inject specific latitude and longitude coordinates to capture accurate delivery fees and geofenced menu pricing.
Compare standard delivery fees against DashPass rates, service fee percentages, and small order minimums.
Extract active storefront banners, percentage discounts, BOGO offers, and minimum spend requirements.
Capture aggregate ratings, review counts, operating hours, and precise store coordinates.
Track dynamic minimum and maximum delivery time windows based on simulated buyer locations.
Identify virtual brands operating out of shared commercial kitchens by correlating store coordinates and addresses.
Configure continuous pipelines at daily or hourly cadences to track menu price inflation and fee changes.
Brief in. Clean data out.
Provide target zip codes, store URLs, or cuisine types. We design the extraction schema together.
We configure Playwright interceptors, proxy rotation, and coordinate spoofing for doordash.com.
Schema validation, modifier depth checks, and fee extraction verification before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or via Webhook on your cadence.
DoorDash uses aggressive bot mitigation and location-gated endpoints. Here is how we maintain data flow.
DoorDash actively blocks headless browsers and data centre IPs. We deploy residential proxies and full Playwright sessions with humanised interaction patterns to clear JavaScript challenges.
Rather than scraping the DOM, our pipeline intercepts DoorDash's internal GraphQL queries. This provides cleaner, heavily structured JSON responses for complex menu hierarchies.
Delivery fees and store availability change by the block. We inject specific latitude and longitude coordinates into the session to simulate buyers in exact target neighbourhoods.
Restaurant menus are deeply nested. We recursively traverse modifier groups to capture all customisation options, default selections, and upcharge values without missing edge cases.
Aggressive crawling triggers IP bans. We distribute requests across thousands of residential nodes, pacing queries to match normal user browsing behaviour.
Restaurant groups monitor rival menu pricing, modifier upcharges, and promotional strategies across specific postcodes.
Aggregators and logistics companies track DoorDash service fees, surge pricing, and DashPass adoption rates.
Ghost kitchens and franchise operators map store density and cuisine gaps to select optimal locations for new sites.
F&B brands analyse category structures and modifier configurations of top-performing restaurants to improve their own conversion rates.
Point-of-sale and packaging vendors extract operating restaurants, ratings, and contact points to build targeted sales lists.
Financial analysts monitor base price changes across standard menu items to track regional food inflation indices.
"DoorDash's location-based pricing and nested modifier menus create one of the most complex public datasets in food delivery. It cannot be mapped without precise coordinate targeting."
Extracting food delivery data requires more than simple HTTP requests. DoorDash relies on dynamic GraphQL endpoints, strict rate limits, and aggressive bot protection. DataFlirt manages the coordinate spoofing, session tokens, and proxy rotation so your engineers can focus on menu analytics instead of infrastructure maintenance.
Everything supported by our doordash.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.
We bypass the DOM entirely where possible, intercepting DoorDash's Next.js hydration state and GraphQL queries to extract clean, structured data directly from the transport layer.
Our residential proxy pools are mapped to specific city blocks. We route requests through IPs that physically match the injected delivery coordinates to avoid location-mismatch flags.
When DoorDash forces a bot challenge, our pipeline pauses, routes the session to a solver or human queue, clears the challenge, and resumes extraction without dropping records.
Data delivered to where your team already works — no new tooling required.
About doordash.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from DoorDash is generally permissible under applicable law. DataFlirt extracts only public, non-authenticated restaurant, menu, and fee data. We do not extract personal user data or circumvent authentication walls.
We use coordinate spoofing combined with geographically matched residential proxies. You provide the target latitude and longitude, and our pipeline simulates a user browsing from that exact location to capture accurate delivery fees and availability.
Yes. Our parsers recursively map nested modifier groups, including minimum and maximum selection limits, default choices, and additional pricing tiers for highly customisable items.
Yes. We extract both the standard delivery fee and the reduced DashPass fee, along with service fee percentages and small order minimums.
Pipelines can be configured for daily, weekly, or monthly refreshes. For specific competitive intelligence use cases, we can run high-frequency hourly tracks on a targeted list of storefronts.
Our smallest packages start at a defined list of target locations or zip codes with weekly delivery. We price based on the volume of storefronts and the frequency of extraction.
Yes. We provide a sample run of up to 50 storefronts in your target area as part of the scoping process. This allows your engineering team to validate the menu schema and modifier depth before signing a contract.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off menu dump for market research or a continuous price-monitoring feed across 10,000 locations, we build and operate the pipeline. Tell us what you need.
