Lever to DuckDB Integration

How Datrise loads Lever into DuckDB

Datrise syncs Lever's candidates, opportunities, postings, interviews, offers, and feedback forms into DuckDB as a typed table per source entity in a DuckDB file. Flexible or custom fields land in JSON or STRUCT columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP WITH TIME ZONE.

Sync is incremental: Datrise uses rewrites changed entities into the local database (or Parquet) on each run, so re-runs update only what changed. Hive-partitioned Parquet by load date when exporting. DuckDB is single-writer and embedded, so Datrise produces a consistent file snapshot rather than concurrent streaming writes.

Ideal for local and notebook analytics without standing up a server.

Endpoints

Lever: Applicant tracking and candidate relationship management.

DuckDB: In-process analytics database for fast local OLAP.

How Lever entities map to DuckDB

Lever entity	DuckDB object	Notes
candidates	`lever_candidates`	id PK · custom fields → JSON or STRUCT columns
opportunities	`lever_opportunities`	id PK · linked to lever_candidates
postings	`lever_postings`	id PK · linked to lever_candidates
interviews	`lever_interviews`	id PK · linked to lever_candidates

FAQ

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

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

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

It runs incrementally — Datrise uses rewrites changed entities into the local database (or Parquet) on each run.

Related pipelines

Early access

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