DatriseAI-first ETL

Greenhouse to Snowflake Integration

AI-first ETL from Greenhouse into Snowflake. Governed entities, incremental sync, typed landing tables.

How Datrise loads Greenhouse into Snowflake

Datrise syncs Greenhouse's candidates, applications, jobs, interviews, scorecards, and offers into Snowflake as a typed table per source entity. Flexible or custom fields land in VARIANT columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP_TZ.

Sync is incremental: Datrise uses staged loads merged on stable id with MERGE, so credits scale with change volume, not table size, so re-runs update only what changed. Automatic micro-partitioning, with optional clustering keys on high-cardinality ids. Snowflake upper-cases unquoted identifiers, so Datrise standardizes on lower-case quoted names to keep column references stable.

Ideal for central analytics warehouses feeding BI and AI workloads.

Endpoints

Greenhouse: Applicant tracking and structured hiring.

Snowflake: Cloud data warehouse with separated compute and storage.

How Greenhouse entities map to Snowflake

Greenhouse entitySnowflake objectNotes
candidatesgreenhouse_candidatesid PK · custom fields → VARIANT columns
applicationsgreenhouse_applicationsid PK · linked to greenhouse_candidates
jobsgreenhouse_jobsid PK · linked to greenhouse_candidates
interviewsgreenhouse_interviewsid PK · linked to greenhouse_candidates

FAQ

How does Datrise handle Greenhouse's custom fields in Snowflake?

Flexible values are stored as VARIANT columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Snowflake types.

How does the Greenhouse to Snowflake sync stay up to date?

It runs incrementally — Datrise uses staged loads merged on stable id with MERGE, so credits scale with change volume, not table size.

Related pipelines

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