Greenhouse to Neon Integration
AI-first ETL from Greenhouse into Neon. Governed entities, incremental sync, typed landing tables.
How Datrise loads Greenhouse into Neon
Datrise syncs Greenhouse's candidates, applications, jobs, interviews, scorecards, and offers into Neon as a typed table per source entity. Flexible or custom fields land in jsonb columns, and timestamps such as created, updated, and status changes are typed as timestamptz.
Sync is incremental: Datrise uses a watermark on updated-at, applied with INSERT … ON CONFLICT DO UPDATE, so re-runs update only what changed. Optional declarative partitioning by load date. Neon separates compute from storage, so Datrise batches writes to keep autoscaling compute from cold-starting on every small change.
Ideal for serverless Postgres workloads that scale to zero between syncs.
Endpoints
Greenhouse: Applicant tracking and structured hiring.
Neon: Serverless Postgres destination with branching and autoscaling.
How Greenhouse entities map to Neon
| Greenhouse entity | Neon object | Notes |
|---|---|---|
| candidates | greenhouse_candidates | id PK · custom fields → jsonb columns |
| applications | greenhouse_applications | id PK · linked to greenhouse_candidates |
| jobs | greenhouse_jobs | id PK · linked to greenhouse_candidates |
| interviews | greenhouse_interviews | id PK · linked to greenhouse_candidates |
FAQ
How does Datrise handle Greenhouse's custom fields in Neon?
Flexible values are stored as jsonb columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Neon types.
How does the Greenhouse to Neon sync stay up to date?
It runs incrementally — Datrise uses a watermark on updated-at, applied with INSERT … ON CONFLICT DO UPDATE.
Related pipelines
More destinations for Greenhouse
Early access
Connect Greenhouse to Neon the easy way
Skip brittle scripts and manual exports. Join the waitlist to get a guided setup, AI-assisted mapping, and reliable incremental sync for this integration.