We extract job postings, req IDs, location hierarchies, and department structures across enterprise Workday tenants. 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 Job Postings objects from workday.com. All fields typed and schema-versioned.
"req_id": "REQ-49218", "title": "Senior Infrastructure Engineer", "tenant_name": "acme-corp", "posted_date": "2026-03-14T08:00:00Z", "time_type": "Full time", "job_family": "Engineering", "worker_sub_type": "Regular"
| # | req_id | title | tenant_name | location | posted_date | job_family |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Location Data objects from workday.com. All fields typed and schema-versioned.
"location_id": "LOC-092", "city": "Bengaluru", "state": "Karnataka", "country": "India", "remote_eligible": true, "location_type": "Corporate Office", "site_name": "Bengaluru Tech Hub"
| # | location_id | city | state | country | postal_code | remote_eligible |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Company & Tenant objects from workday.com. All fields typed and schema-versioned.
"tenant_name": "acme-corp", "career_site_id": "external", "total_active_jobs": 412, "primary_language": "en-US", "domain_url": "acmecorp.myworkdayjobs.com", "workday_version": "v2026.1", "last_scraped": "2026-05-12T09:14:00Z"
| # | tenant_name | career_site_id | total_active_jobs | primary_language | domain_url | workday_version |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Requirements objects from workday.com. All fields typed and schema-versioned.
"education_level": "Bachelor's Degree", "years_experience": "5+", "skills_list": "['Python', 'Kubernetes', 'PostgreSQL']", "travel_pct": "10%", "clearance_required": false, "languages": "['English']", "background_check": true
| # | education_level | years_experience | skills_list | certifications | travel_pct | clearance_required |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Categories & Meta objects from workday.com. All fields typed and schema-versioned.
"job_category": "Information Technology", "posting_status": "Active", "external_url": "https://acmecorp.myworkdayjobs.com/en-US/external/job/REQ-49218", "apply_url": "https://acmecorp.myworkdayjobs.com/en-US/external/job/REQ-49218/apply", "internal_req_flag": false, "scrape_timestamp": "2026-05-12T09:14:33Z", "hash_id": "a1b2c3d4e5f6"
| # | job_category | sub_category | posting_status | time_to_fill_estimate | external_url | apply_url |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Workday operates as a decentralised platform. Every company has a unique tenant domain, custom fields, and strict API constraints. Our pipeline normalises this chaos into a single predictable schema.
Identify and track active myworkdayjobs.com domains across thousands of enterprise companies automatically.
Bypass fragile DOM parsing. We intercept the undocumented JSON endpoints Workday uses to populate its single-page applications.
Workday APIs often cap results at 10,000 records. We implement dynamic search faceting to bypass these limits and extract full catalogues.
Extract local language job descriptions and metadata by manipulating the accept-language headers and locale parameters.
Parse complex Workday location strings into structured city, state, country, and remote-eligibility boolean fields.
Track job lifecycles via static req IDs to calculate time-to-fill metrics and identify ghost jobs.
Maintain state across runs. Receive diffs for newly opened roles, modified descriptions, and closed requisitions.
Unify data despite custom fields. We map tenant-specific metadata into a normalised global schema.
Run continuous pipelines at hourly or daily cadences to capture the exact moment a requisition opens or closes.
Rotate IP addresses per tenant request to avoid WAF blocks and ensure complete data capture without IP bans.
Brief in. Clean data out.
Provide target tenant URLs, company names, or industry filters. We map the required fields and delivery frequency.
We configure API interceptors, CSRF token management, proxy rotation, and schema normalisation logic.
Schema validation, null-rate checks, and cross-tenant normalisation verification before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Workday is heavily protected by strict session management and undocumented APIs. Here is how we maintain reliable extraction.
Workday job boards are single-page applications. Parsing the DOM is slow and brittle. We intercept the underlying JSON POST requests, extracting clean, structured data directly from the source endpoints.
Workday APIs require strict CSRF tokens and session cookies to function. Our infrastructure manages token generation, cookie jars, and session refresh cycles automatically across thousands of concurrent tenant connections.
Large enterprise tenants cap API responses at a fixed number of jobs. We dynamically facet requests by location, job family, and posting date to force the API to return the complete dataset without hitting pagination limits.
Every company configures Workday differently, adding custom fields and unique data structures. Our normalisation engine maps these variations into a single predictable schema, ensuring downstream compatibility.
Tenant endpoints are protected by rate limits and web application firewalls. We route requests through residential proxy pools, rotating IPs per tenant to maintain high throughput without triggering defensive blocks.
Analyse hiring trends, skill demand shifts, and geographic expansion patterns across the Fortune 500.
Monitor competitor requisitions to identify strategic shifts, new product teams, and executive departures.
Identify companies hiring for specific roles or software skills to trigger highly targeted sales outreach.
Populate niche job boards with high-quality, direct-employer listings without relying on third-party aggregators.
Extract posted salary ranges from job descriptions to build accurate compensation models across industries.
Track headcount growth and department expansion velocity to evaluate company health prior to investment.
"Workday hosts the hiring data for the Fortune 500, but its fragmented, tenant-specific architecture makes aggregate analysis impossible without a unified extraction layer."
Extracting Workday data requires reverse-engineering undocumented JSON APIs, managing strict CSRF tokens, and handling custom data schemas across thousands of enterprise tenants. DataFlirt abstracts this complexity. We maintain the API interceptors and proxy pools so you receive clean, normalised job records directly in your warehouse.
Everything supported by our workday.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 traditional HTML parsing. Our engine replicates browser network requests, handling complex headers and CSRF tokens to query Workday internal APIs directly for maximum speed and reliability.
Workday rate limits aggressively per tenant. We distribute requests across massive residential IP pools, allowing us to scrape thousands of tenant domains concurrently without triggering WAF blocks.
Pipelines run on AWS Lambda and ECS. Airflow manages scheduling, dependency resolution, and retry logic. State and diff hashes are stored in managed PostgreSQL to ensure accurate change detection.
Data delivered to where your team already works — no new tooling required.
About workday.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available job postings is generally permissible under applicable law, reinforced by rulings like hiQ v. LinkedIn. DataFlirt extracts only public, non-authenticated job data. We do not bypass authentication walls or extract private employee data. Clients should consult legal counsel for their specific use cases.
Our normalisation engine maps standard fields like title, location, and description automatically. For custom tenant metadata, we extract it into a nested JSON object within the payload, preserving the raw data while maintaining a clean top-level schema.
Yes. When a tenant exceeds API pagination limits, our pipeline dynamically applies search facets like location, job family, and posting date to divide the catalogue into smaller, retrievable chunks, ensuring 100% extraction coverage.
No. We extract data from the public-facing myworkdayjobs.com career sites using the same endpoints accessed by standard web browsers. No official API credentials or partner agreements are required.
We configure extraction cadences based on your requirements. Typical pipelines run daily, but we support hourly runs for high-frequency use cases. Change detection logic ensures you only receive updates for new, modified, or closed requisitions.
Yes. We maintain a hash index of all active requisitions per tenant. If a requisition ID disappears from the active API response, we flag it as closed and emit a state change record in the next delivery batch.
Yes, where the salary range is exposed in the API response or job description text. We extract this data and normalise it into minimum, maximum, and currency fields.
Our minimum engagement starts at a defined list of target tenants or a specific industry vertical. We price based on the volume of tenants tracked and the frequency of extraction. Contact us for a scoped quote.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off export of a specific tenant or continuous monitoring across thousands of enterprise companies, we build and operate the pipeline. Tell us what you need.
