DatriseAI-first ETL

Lever to Looker Integration

AI-first ETL from Lever into Looker. Governed entities, incremental sync, typed landing tables.

How Datrise loads Lever into Looker

Datrise syncs Lever's candidates, opportunities, postings, interviews, offers, and feedback forms into Looker as governed warehouse tables with LookML-ready naming. Flexible or custom fields land in flattened columns (nested fields expanded for modeling), and timestamps such as created, updated, and status changes are typed as date/time dimension columns.

Sync is incremental: Datrise uses incremental refresh of the underlying warehouse tables Looker explores, so re-runs update only what changed. Date-partitioned fact tables for PDT performance. Looker models live in LookML on top of SQL, so Datrise lands clean, stable column names rather than churn that would break your views.

Ideal for governed, version-controlled BI on a warehouse.

Endpoints

Lever: Applicant tracking and candidate relationship management.

Looker: Google Cloud BI with LookML semantic models and governed dashboards.

How Lever entities map to Looker

Lever entityLooker objectNotes
candidateslever_candidatesid PK · custom fields → flattened columns (nested fields expanded for modeling)
opportunitieslever_opportunitiesid PK · linked to lever_candidates
postingslever_postingsid PK · linked to lever_candidates
interviewslever_interviewsid PK · linked to lever_candidates

FAQ

How does Datrise handle Lever's custom fields in Looker?

Flexible values are stored as flattened columns (nested fields expanded for modeling), so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Looker types.

How does the Lever to Looker sync stay up to date?

It runs incrementally — Datrise uses incremental refresh of the underlying warehouse tables Looker explores.

Related pipelines

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