Overview
The client needed to extract recruiting and applicant tracking data from iCIMS and centralize it in Azure Data Lake Gen2. iCIMS is a cloud-based ATS platform, but the data needed for reporting was not available as a traditional SQL database source. The integration required an API-driven approach.
Challenge
- iCIMS could not be treated like a database source — REST/HTTP extraction only.
- Two-step extraction needed: discover IDs, then call detail endpoints.
- Required pagination, profile-level extraction, and scalable expansion to more objects.
Solution
MSPowerhouse designed an Azure Data Factory ingestion pattern for iCIMS using REST/HTTP-based extraction. The architecture followed a staged approach: first identify available records or IDs, then loop through those IDs to pull detailed records, then land the raw output into Azure Data Lake Gen2.
The pipeline pattern was designed to support pagination, profile-level extraction, and future expansion to additional iCIMS objects. Raw JSON was preserved in the lake so that downstream reporting teams could transform the data without repeatedly calling the source system.
Technical Execution
- Azure Data Factory REST/HTTP source configuration.
- iCIMS API authentication planning.
- Search API pattern to retrieve system IDs.
- Detail endpoint calls for candidate, job, or profile records.
- Parent-child pipeline structure.
- ForEach activity for record-level extraction.
- Pagination handling.
- Raw JSON storage in ADLS Gen2.
- Optional curated output for Power BI or analytics.
- Reusable framework for future ATS objects.
Outcome
The client received a practical integration design for bringing iCIMS recruiting data into Azure. Instead of treating iCIMS like a database, MSPowerhouse correctly positioned it as a SaaS API integration and designed the pipeline accordingly.
Impact
This project helped the client move recruiting data into a centralized Azure data platform while reducing manual exports and creating a scalable pattern for future HR and recruiting analytics.
