Sync Recruiterflow Data
to Flat Files
& Spreadsheets
Automatically sync your Recruiterflow data to cloud storage and spreadsheets with Flatly's no-code, triggerless, turnkey integration service.
About the integration: Recruiterflow is an all-in-one ATS + CRM + Automation built for executive search firms and recruiting agencies. Flatly integrates with Recruiterflow's official APIs.
Available Datasets
Flatly can sync the following datasets from Recruiterflow.
- Candidates
- Candidates-Last X days Date-ranged
- Clients
- Clients-Last X days Date-ranged
- Contacts
- Contacts-Last X days Date-ranged
- Jobs
- Jobs-Last X days Date-ranged
Send Recruiterflow data to
Flatly can sync Recruiterflow data to any of these destinations.
- Airtable Airtable
- Amazon S3 XLSX (Parsed) XLSX (Raw) CSV (Parsed) CSV (Raw) NDJSON Markdown (Table) Markdown (Key-Value)
- Azure Storage XLSX (Parsed) XLSX (Raw) CSV (Parsed) CSV (Raw) NDJSON Markdown (Table) Markdown (Key-Value)
- Box XLSX (Parsed) XLSX (Raw) CSV (Parsed) CSV (Raw) NDJSON Markdown (Table) Markdown (Key-Value)
- CSV
- Cloud Storage
- Dropbox XLSX (Parsed) XLSX (Raw) CSV (Parsed) CSV (Raw) NDJSON Markdown (Table) Markdown (Key-Value)
- Excel
- Excel Online XLSX (Parsed) XLSX (Raw) CSV (Parsed) CSV (Raw) NDJSON Markdown (Table) Markdown (Key-Value)
- FTP XLSX (Parsed) XLSX (Raw) CSV (Parsed) CSV (Raw) NDJSON Markdown (Table) Markdown (Key-Value)
- Google BigQuery NDJSON
- Google Cloud Storage XLSX (Parsed) XLSX (Raw) CSV (Parsed) CSV (Raw) NDJSON Markdown (Table) Markdown (Key-Value)
- Google Drive Google Sheets (Parsed) Google Sheets (Raw) XLSX (Parsed) XLSX (Raw) CSV (Parsed) CSV (Raw) NDJSON Markdown (Table) Markdown (Key-Value)
- Google Looker Studio
- Google Sheets Google Sheets (Parsed) Google Sheets (Raw) XLSX (Parsed) XLSX (Raw) CSV (Parsed) CSV (Raw) NDJSON Markdown (Table) Markdown (Key-Value)
- Looker NDJSON
- Markdown
- Microsoft Power BI
- OneDrive XLSX (Parsed) XLSX (Raw) CSV (Parsed) CSV (Raw) NDJSON Markdown (Table) Markdown (Key-Value)
- OneDrive for Business XLSX (Parsed) XLSX (Raw) CSV (Parsed) CSV (Raw) NDJSON Markdown (Table) Markdown (Key-Value)
- SFTP XLSX (Parsed) XLSX (Raw) CSV (Parsed) CSV (Raw) NDJSON Markdown (Table) Markdown (Key-Value)
- SharePoint XLSX (Parsed) XLSX (Raw) CSV (Parsed) CSV (Raw) NDJSON Markdown (Table) Markdown (Key-Value)
- Smartsheet Smartsheet
- Zoho Sheet Zoho Sheet
Note: Some destinations are aliased, for intents and purposes they are functionally equivalent.
Querying Recruiterflow - AI or ETL?
Many SaaS applications and databases can be directly queried within AI chat apps using connectors. For ad-hoc, single-user requests, this direct architecture is superior—it offers realtime visibility, high customizability, and bypasses the need for complex data pipelines.
However, ETL (Extract, Transform, Load) can be more favorable when certain requirements exist. You might consider transitionining to an ETL system and a centralized data store when:
- Collaboration is required: Multiple stakeholders (colleagues, partners) need consistent access to the same shared single source of truth.
- Downstream audiences: The queried data must be routed into other internal IT systems, published to team dashboards, or preserved in official records rather than remaining isolated in a chat window.
- Data is complex: The raw data needs heavy cleaning or joining before an AI or non-technical user can accurately understand it.
- Performance matters: Querying the live SaaS tool directly would hit API limits or slow down production systems.
- Internal capabilities are limited: Your organization lacks the in-house programming resources or infrastructure necessary to reliably build, deploy, and maintain custom agentic workflows or complex data integrations from scratch.