Real-time
Two-way sync
Changes in Greenhouse or Render Postgres instantly reflect in both systems. No stale data, no manual imports.
Human resources ⇄ Database
Greenhouse to Render Postgres integration — real-time, two-way sync
Keep Greenhouse and Render 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.
- SOC 2 and 6 other compliance frameworks
- POC with real engineers in minutes
Adopted by fast-scaling companies moving mission-critical data in real time
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Migrated from Fivetran
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Migrated from Celigo
Why teams connect Greenhouse and Render Postgres
Put your workforce data where your apps can reach it: Render 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. Render Postgres is where internal tools, dashboards, provisioning jobs, and analytics actually read and store records. The overlap is the workforce itself: Scheduled Interviews, Users, Departments and Offices, Candidates in Greenhouse need to exist as queryable Schemas, Columns and Types, Indexes and Constraints, Tables in Render 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 Schemas, Columns and Types, Indexes and Constraints, Tables in Render Postgres with Scheduled Interviews, Users, Departments and Offices, Candidates 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 Capture row-level changes via logical replication and propagate them to a warehouse or another database in near real time.
- 02 Serve as the operational read/write store behind internal tools while Stacksync keeps it consistent with SaaS systems of record.
- 03 Stream Jobs, Departments, and Offices into a reporting database for recruiting funnel and time-to-fill dashboards.
- 04 Export Scorecards and Scheduled Interviews to a data warehouse to analyze interviewer load and interview outcomes.
Common sync patterns
Mirror people records into the database
Records maintained in Greenhouse land as queryable Schemas, Columns and Types, Indexes and Constraints, Tables in Render Postgres, so internal apps and dashboards read live data instead of a periodic export.
One directory of record
When a person record is added, changed, or deactivated in either system, the matching row in the other stays current, ending dual maintenance.
Reporting and analytics on current data
Scheduled Interviews, Users, Departments and Offices, Candidates replicate into Render Postgres where they join operational tables, so headcount, roles, and status reports run on the live state without manual pulls.
What you can sync between Greenhouse and Render 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 | Render Postgres objects | How this pairing syncs | |
|---|---|---|---|
| Departments and Offices Org structure attached to Jobs; read and written to keep reporting hierarchies aligned with an HRIS or warehouse. | Indexes and Constraints Primary keys, unique constraints, and foreign keys; unique keys drive idempotent upserts and conflict resolution during sync. | Departments and Offices is specific to Greenhouse and Indexes and Constraints to Render Postgres — 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. | Tables Relational tables with full column typing; synced two-way with CRMs, ERPs, and SaaS apps so application data is queryable as plain Postgres rows. | Candidates is specific to Greenhouse and Tables to Render Postgres — 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. | Views Saved queries exposed as read-only relations; read out to BI tools or downstream syncs without duplicating transformation logic. | Applications is specific to Greenhouse and Views to Render Postgres — 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. | Materialized Views Precomputed query results refreshed on demand; read for fast reporting tables that downstream systems can consume. | Jobs is specific to Greenhouse and Materialized Views to Render 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. | Schemas Namespaces that organize tables per app or environment; sync targets are scoped per schema to keep synced data isolated and tidy. | Offers is specific to Greenhouse and Schemas to Render 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. | Columns and Types Full Postgres type system including JSONB and arrays; field mappings preserve native types instead of flattening to strings. | Scorecards is specific to Greenhouse and Columns and Types to Render Postgres — each maps to any object or custom field on the other side. |
How changes propagate between Greenhouse and Render 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 Render 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 Render Postgres as a row-level write, with types converted between the two schemas.
Render Postgres Greenhouse Sub-second propagation
DetectionChanges in Render Postgres are captured at the source via change data capture — no polling loop against its API. Logical replication via WAL and replication slots for change data capture when enabled on the instance, with timestamp or cursor-based polling as the.
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.
- Render Postgres: No API rate limits — throughput is bounded by the instance's plan (CPU, RAM, connection limit); connection pooling is recommended since managed plans cap concurrent connections.
What ships with Greenhouse ⇄ Render Postgres
Connect Greenhouse and Render Postgres for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Greenhouse–Render Postgres connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever Greenhouse or Render 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 Render Postgres record.
Observability
Monitoring
Track your Greenhouse ⇄ Render 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 Render Postgres.
How the Greenhouse and Render 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.
Render Postgres
- Integration surface
- PostgreSQL wire protocol (managed Postgres on Render)
- Authentication
- Standard Postgres connection string — host, port, database, user, password with TLS; Render provides internal and external connection URLs and IP allowlisting
- Change detection
- Logical replication via WAL and replication slots for change data capture when enabled on the instance, with timestamp or cursor-based polling as the fallback
- Capabilities
- read · write · CDC
- Rate limits
- No API rate limits — throughput is bounded by the instance's plan (CPU, RAM, connection limit); connection pooling is recommended since managed plans cap concurrent connections.
How it works
How to connect Greenhouse to Render 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.
- 01
Connect your apps
Authenticate Greenhouse and Render Postgres with each platform's native method — OAuth, API keys, or service accounts — plus secure options like SSH tunneling, IP whitelisting, and VPC peering.
Greenhouse
connected
Render Postgres
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the Greenhouse and Render 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.
objects · Greenhouse ⇄ Render PostgresCustomers 12,480Sales Orders 8,213Invoices 5,902Items 1,344 - 03
Map fields
Fields map automatically even when names and types differ. Stacksync handles transformation and type casting for you, zero configuration required.
Greenhouse Render PostgresCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
Greenhouse and Render Postgres integration FAQ
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Yes. Stacksync provides a managed, real-time two-way integration between Greenhouse and Render Postgres: authenticate both systems, choose the objects to sync (such as Greenhouse's Departments and Offices and Candidates), map fields visually, and changes propagate both ways in milliseconds — no code required.
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Stacksync is SOC 2 Type II and ISO 27001 certified with HIPAA BAA support. Data is encrypted in transit, and a zero-persistent-storage architecture means Greenhouse and Render Postgres records are not retained after a sync operation.
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Stacksync pricing is usage-based and starts at $1,000/month, including the managed Greenhouse and Render Postgres connectors, real-time two-way sync, monitoring, and support. That replaces building and maintaining a custom Greenhouse–Render Postgres integration in-house.
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Yes — Stacksync ships production-grade connectors for both Greenhouse and Render Postgres. The connectors handle authentication, schema detection, rate limits, and retries; you configure the sync, and Stacksync operates it.
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Change detection on Greenhouse: HMAC-SHA256 signed webhooks for candidate, application, job, and interview events, plus polling list endpoints with created_after / updated_after / last_activity_after filters. On Render Postgres: Logical replication via WAL and replication slots for change data capture when enabled on the instance, with timestamp or cursor-based polling as the fallback. Each detected change propagates to the other side in milliseconds, with field-level conflict resolution and an inspectable event log.
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On the Render Postgres side: Schemas, Columns and Types, Indexes and Constraints, Tables, plus custom fields where Render Postgres exposes them. On the Greenhouse side: Scheduled Interviews, Users, Departments and Offices, Candidates. Stacksync auto-detects both schemas and converts types between the two systems.
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.
SOC 2 Type II
ISO 27001
HIPAA BAA
GDPR
CCPA
DPF US-EU-UK-CH
→ SECURITY WITH BENEFITS
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:
Related integrations
Every pair below is a real-time, two-way sync. Search all 416 integrations available for Greenhouse and Render Postgres.
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Greenhouse ⇄ MariaDB -
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