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Human resources ⇄ Data warehouse

Greenhouse to Snowflake integration — real-time, two-way sync

Keep Greenhouse and Snowflake in sync without custom scripts. Cut weeks of integration work, eliminate silent data drift, and give your team a single, reliable source of truth.

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Why teams connect Greenhouse and Snowflake

Land the people and organization records from Greenhouse in Snowflake as live tables for workforce reporting, without extract jobs, and write computed results back where Greenhouse can use them.

Workforce data is some of the most requested data in the warehouse and some of the most awkward to move: the record types are many, the fields carry sensitive personal information, the APIs are strict, and hand-built extract jobs go stale or break quietly. Whether Greenhouse is the system of record for employees and payroll, for candidates and applications, or for learners and course completions, the reporting belongs in Snowflake next to everything else the company measures.

Stacksync syncs Jobs, Offers, Scorecards, Scheduled Interviews from Greenhouse into tables in Snowflake continuously, handling API limits and schema drift as they come. The connection is bi-directional, so values computed in Snowflake, such as headcount rollups, cost allocations, or attrition risk flags, can be written back to fields in Greenhouse where the HR team acts on them. You configure which records and fields cross over, and in which direction, instead of maintaining pipeline code.

Common use cases

  • 01 Land CRM and ERP records in Snowflake continuously so BI reflects business systems without nightly batch ETL
  • 02 Activate modeled Snowflake tables by syncing scores and attributes back into CRM fields sales can act on
  • 03 Two-way sync Candidates and Applications with Postgres so recruiting-ops apps read and update stage, status, and custom fields in SQL while recruiters stay in Greenhouse.
  • 04 Write enriched or sourced Candidates from external tools into Greenhouse and keep contact fields refreshed as data changes.

Common sync patterns

HR data in the warehouse, minus the pipeline

People and organization records from Greenhouse arrive in Snowflake as queryable tables, current within seconds instead of a day behind, replacing hand-built extract jobs.

Headcount and cost joined with everything else

Analysts combine Greenhouse's workforce records with finance, product, or operational data already in Snowflake for reporting the HR system cannot produce on its own.

Fresh data instead of last night's load

Because changes stream continuously, reports query current workforce data rather than waiting for an overnight load window to finish.

What you can sync between Greenhouse and Snowflake

Representative objects on each side — any object or custom field can map to any target. Schemas are auto-detected; types are converted between the two systems.

Greenhouse objects Snowflake objects How this pairing syncs
Users Greenhouse users (recruiters, hiring managers); read and written, and referenced by the On-Behalf-Of header on every write. Schemas Namespaces within a database used to organize synced tables. Users is specific to Greenhouse and Schemas to Snowflake — each maps to any object or custom field on the other side.
Departments and Offices Org structure attached to Jobs; read and written to keep reporting hierarchies aligned with an HRIS or warehouse. Tables The main landing and activation target for synced records. Departments and Offices is specific to Greenhouse and Tables to Snowflake — each maps to any object or custom field on the other side.
Candidates Person records with contact details, tags, and custom fields; read and written via Harvest, often created from sourcing or enrichment pipelines. Views Modeled projections used as the source side of outbound syncs. Candidates is specific to Greenhouse and Views to Snowflake — each maps to any object or custom field on the other side.
Applications Links a Candidate to a Job; carries current stage, status, and source. Movable, rejectable, and hireable through Harvest write calls. Materialized Views Precomputed results synced outward for low-latency reads. Applications is specific to Greenhouse and Materialized Views to Snowflake — each maps to any object or custom field on the other side.
Jobs Requisitions with openings, hiring team, departments, and offices; created and patched via Harvest, read out for funnel and time-to-fill reporting. Streams Row-level change records on a table, consumed to process deltas instead of full scans. Jobs is specific to Greenhouse and Streams to Snowflake — each maps to any object or custom field on the other side.
Offers Offer records tied to an Application with status and custom offer fields; readable and patchable, commonly synced to HRIS on hire. Stages File staging areas used for bulk loads into synced tables. Offers is specific to Greenhouse and Stages to Snowflake — each maps to any object or custom field on the other side.

How changes propagate between Greenhouse and Snowflake

Each direction of the sync is driven by what the source system can signal and what the destination accepts — detection, delivery, and expected latency below.

Greenhouse Snowflake Sub-second propagation

DetectionGreenhouse notifies Stacksync of record changes through webhook events. HMAC-SHA256 signed webhooks for candidate, application, job, and interview events, plus polling list endpoints with created_after / updated_after /.

DeliveryEach detected change is applied to Snowflake as a row-level write, with types converted between the two schemas.

Snowflake Greenhouse Sub-second propagation

DetectionChanges in Snowflake are captured at the source via change data capture — no polling loop against its API. The setup script grants "create stream" on synced schemas (Snowflake streams), but the docs do not name the change-capture mechanism.

DeliveryEach detected change is written to Greenhouse through its API, with automatic retries and rate-limit backoff.

Rate-limit considerations

  • Greenhouse: Harvest enforces a per-integration limit over a rolling 10-second window (X-RateLimit-Limit, commonly 50 requests / 10s for approved integrations); responses carry X-RateLimit-Remaining and, on a 429, X-RateLimit-Reset and Retry-After.
  • Snowflake: No conventional API rate limits; cost and throughput are governed by virtual warehouse size and running time.
What ships with Greenhouse ⇄ Snowflake

Connect Greenhouse and Snowflake for flexible, real-time data sync.

Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Greenhouse–Snowflake connection.

Real-time

Two-way sync

Changes in Greenhouse or Snowflake instantly reflect in both systems. No stale data, no manual imports.

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Greenhouse or Snowflake data changes, update records, fire webhooks, or kick off sequences without brittle API scripts.

At scale

Event queues

Handle millions of events per minute without losing a single Greenhouse or Snowflake record.

Observability

Monitoring

Track your Greenhouse ⇄ Snowflake sync health, view errors, and replay failed events in one click.

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between Greenhouse and Snowflake.

How the Greenhouse and Snowflake connectors work

Greenhouse

Integration surface
Harvest REST API (plus read-only Job Board API and the Ingestion API for bulk candidate import)
Authentication
HTTP Basic Auth with a Harvest API key (key as username, blank password, colon appended then Base64-encoded); write calls require an On-Behalf-Of header naming the Greenhouse user
Change detection
HMAC-SHA256 signed webhooks for candidate, application, job, and interview events, plus polling list endpoints with created_after / updated_after / last_activity_after filters
Capabilities
read · write · webhooks
Rate limits
Harvest enforces a per-integration limit over a rolling 10-second window (X-RateLimit-Limit, commonly 50 requests / 10s for approved integrations); responses carry X-RateLimit-Remaining and, on a 429, X-RateLimit-Reset and Retry-After.
Greenhouse setup guide

Snowflake

Integration surface
SQL via JDBC/ODBC and native drivers, plus the Snowflake SQL REST API
Authentication
Dedicated Snowflake service user + role with RSA key-pair authentication (Stacksync-provided public key), created via a setup script requiring SECURITY_ADMIN and ACCOUNTADMIN roles
Change detection
Not explicitly stated; the setup script grants "create stream" on synced schemas (Snowflake streams), but the docs do not name the change-capture mechanism
Capabilities
read · write · CDC
Rate limits
No conventional API rate limits; cost and throughput are governed by virtual warehouse size and running time
Snowflake setup guide
How it works

How to connect Greenhouse to Snowflake — three steps, no code

Configure and sync within minutes, no code. Whether you sync 50k or 100M+ records, Stacksync handles the queues, infra, and plumbing. Integrations are non-invasive and need zero setup on your systems.

  1. 01

    Connect your apps

    Authenticate Greenhouse and Snowflake with each platform's native method — OAuth, API keys, or service accounts — plus secure options like SSH tunneling, IP whitelisting, and VPC peering.

    • OAuth 2.0
    • SSH tunnel
    • VPC peering
    Greenhouse connected
    Snowflake connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Greenhouse and Snowflake objects to sync — Stacksync auto-detects both schemas, including custom fields where the platform exposes them. Sync to existing tables, or let Stacksync create new ones with ideal data types.

    • Standard objects
    • Custom objects
    • Auto-schema
    objects · Greenhouse ⇄ Snowflake
    Customers 12,480
    Sales Orders 8,213
    Invoices 5,902
    Items 1,344
  3. 03

    Map fields

    Fields map automatically even when names and types differ. Stacksync handles transformation and type casting for you, zero configuration required.

    • Auto-map
    • Type casting
    • Transforms
    Greenhouse Snowflake
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

Greenhouse and Snowflake integration FAQ

SECURITY

Security teams trust Stacksync

As a data company, we understand the importance of keeping your data secure. Stacksync is built with security best practices to keep your data safe at every layer, and is DPF-certified for US, EU, UK and CH data transfers.

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SSO & SCIM

Let your users access Stacksync from your centralized user management systems. Works with Okta, Azure, Google SSO and more.

Alerts

Immediately get alerted about record syncing issues over email, Slack, PagerDuty and WhatsApp. Resolve issues from a centralized dashboard with retry and revert options.

Secure connection options

Securely connects to your systems with:

OAuth 2 SSH Tunnelling SSL certificates IP Whitelisting VPN gateway VPC peering and more

Related integrations

Every pair below is a real-time, two-way sync. Search all 547 integrations available for Greenhouse and Snowflake.

Popular · 8 of 547
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