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Human resources ⇄ Database

Greenhouse to Scaleway Postgres integration — real-time, two-way sync

Keep Greenhouse and Scaleway Postgres 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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Adopted by fast-scaling companies moving mission-critical data in real time

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Why teams connect Greenhouse and Scaleway Postgres

Put your workforce data where your apps can reach it: Scaleway Postgres and Greenhouse share the same people, positions, and org structure in real time.

Greenhouse is the system of record for the people side of the business — employees, candidates, roles, and the org structure around them. Scaleway Postgres is where internal tools, dashboards, provisioning jobs, and analytics actually read and store records. The overlap is the workforce itself: Candidates, Applications, Jobs, Offers in Greenhouse need to exist as queryable Materialized views, Schemas, Sequences, Columns in Scaleway Postgres before an app can act on them. When that bridge is a nightly export or a hand-run CSV, every downstream system spends the day working from a roster that has already moved on.

Stacksync syncs Materialized views, Schemas, Sequences, Columns in Scaleway Postgres with Candidates, Applications, Jobs, Offers in Greenhouse field by field, in real time. You decide which system owns which fields — Greenhouse typically owns identity and org attributes, while operational or computed values can flow back the other way — and Stacksync keeps every copy consistent, matching records on a stable key and resolving conflicts by rules you set.

The result is one live picture of the workforce on both sides: HR keeps its source of truth, and the database keeps a current mirror that internal apps, reports, and access controls can trust without a batch window in between.

Common use cases

  • 01 Back internal tools with the same Postgres instance while Stacksync keeps it consistent with external systems
  • 02 Keep an EU-hosted Postgres as the system of record while distributing data to US-hosted SaaS applications
  • 03 Write enriched or sourced Candidates from external tools into Greenhouse and keep contact fields refreshed as data changes.
  • 04 Stream Jobs, Departments, and Offices into a reporting database for recruiting funnel and time-to-fill dashboards.

Common sync patterns

Reporting and analytics on current data

Candidates, Applications, Jobs, Offers replicate into Scaleway Postgres where they join operational tables, so headcount, roles, and status reports run on the live state without manual pulls.

Org and structure stay aligned

Groups, departments, managers, and reporting lines from Greenhouse stay consistent in Scaleway Postgres, so hierarchy-driven logic and permissions don't drift.

Computed and operational fields flow back

Values assembled or corrected in Scaleway Postgres write onto the matching record in Greenhouse where those fields are writable, keeping the people system enriched.

What you can sync between Greenhouse and Scaleway Postgres

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 Scaleway Postgres objects How this pairing syncs
Jobs Requisitions with openings, hiring team, departments, and offices; created and patched via Harvest, read out for funnel and time-to-fill reporting. Views Read-only sources for shaping data before it leaves the database. Jobs is specific to Greenhouse and Views to Scaleway Postgres — 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. Materialized views Precomputed result sets that can be read on a schedule for downstream syncs. Offers is specific to Greenhouse and Materialized views to Scaleway Postgres — each maps to any object or custom field on the other side.
Scorecards Interviewer feedback and ratings tied to interviews; read-only in Harvest, exported to a warehouse for interview analytics. Schemas Namespace tables so multiple applications or environments can be synced selectively. Scorecards is specific to Greenhouse and Schemas to Scaleway Postgres — each maps to any object or custom field on the other side.
Scheduled Interviews Interview events with interviewers, times, and rooms; full create/update/delete support for calendar and reporting syncs. Sequences Generate primary keys; sync tooling must respect them when writing rows. Scheduled Interviews is specific to Greenhouse and Sequences to Scaleway Postgres — each maps to any object or custom field on the other side.
Users Greenhouse users (recruiters, hiring managers); read and written, and referenced by the On-Behalf-Of header on every write. Columns Postgres-native types, including JSONB and arrays, are mapped to fields in the paired system. Users is specific to Greenhouse and Columns to Scaleway Postgres — 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 Primary sync unit; each table maps to an object or table on the other side of the sync. Departments and Offices is specific to Greenhouse and Tables to Scaleway Postgres — each maps to any object or custom field on the other side.

How changes propagate between Greenhouse and Scaleway Postgres

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 Scaleway Postgres 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 Scaleway Postgres as a row-level write, with types converted between the two schemas.

Scaleway Postgres Greenhouse Sub-second propagation

DetectionChanges in Scaleway Postgres are captured at the source via change data capture — no polling loop against its API. Log-based CDC via PostgreSQL logical replication where the managed instance permits it.

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.
  • Scaleway Postgres: Throughput is bounded by instance size and connection limits rather than API quotas.
What ships with Greenhouse ⇄ Scaleway Postgres

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Greenhouse or Scaleway Postgres 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 Scaleway Postgres record.

Observability

Monitoring

Track your Greenhouse ⇄ Scaleway Postgres 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 Scaleway Postgres.

How the Greenhouse and Scaleway Postgres 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

Scaleway Postgres

Integration surface
SQL wire protocol (PostgreSQL)
Authentication
Database credentials (username/password over TLS)
Change detection
Log-based CDC via PostgreSQL logical replication where the managed instance permits it; otherwise timestamp or query-based polling
Capabilities
read · write · CDC
Rate limits
Throughput is bounded by instance size and connection limits rather than API quotas
How it works

How to connect Greenhouse to Scaleway Postgres — 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 Scaleway Postgres 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
    Scaleway Postgres connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Greenhouse and Scaleway Postgres 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 ⇄ Scaleway Postgres
    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 Scaleway Postgres
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

Greenhouse and Scaleway Postgres 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 443 integrations available for Greenhouse and Scaleway Postgres.

Popular · 7 of 443
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