DatriseAI-first ETL

Greenhouse to Google BigQuery Integration

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

How Datrise loads Greenhouse into Google BigQuery

Datrise syncs Greenhouse's candidates, applications, jobs, interviews, scorecards, and offers into Google BigQuery as a partitioned table per source entity. Flexible or custom fields land in JSON or nested/repeated (STRUCT) columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP.

Sync is incremental: Datrise uses appends to a staging table, then MERGE on stable id into the partitioned target, so re-runs update only what changed. Partition by ingestion or event date and cluster by entity id to keep scanned bytes low. BigQuery bills by bytes scanned, so Datrise partitions and clusters every table to keep query costs predictable.

Ideal for Google-stack analytics and ML on serverless infrastructure.

Endpoints

Greenhouse: Applicant tracking and structured hiring.

Google BigQuery: Serverless analytics warehouse on GCP.

How Greenhouse entities map to Google BigQuery

Greenhouse entityGoogle BigQuery objectNotes
candidatesgreenhouse_candidatesid PK · custom fields → JSON or nested/repeated (STRUCT) 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 Google BigQuery?

Flexible values are stored as JSON or nested/repeated (STRUCT) columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Google BigQuery types.

How does the Greenhouse to Google BigQuery sync stay up to date?

It runs incrementally — Datrise uses appends to a staging table, then MERGE on stable id into the partitioned target.

Related pipelines

Early access

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