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Human resources ⇄ Database

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

Keep Greenhouse and Postgres Heroku 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 Postgres Heroku

Put your workforce data where your apps can reach it: Postgres Heroku 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. Postgres Heroku is where internal tools, dashboards, provisioning jobs, and analytics actually read and store records. The overlap is the workforce itself: Offers, Scorecards, Scheduled Interviews, Users in Greenhouse need to exist as queryable Views, Materialized Views, Schemas, Primary and Unique Keys in Postgres Heroku 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 Views, Materialized Views, Schemas, Primary and Unique Keys in Postgres Heroku with Offers, Scorecards, Scheduled Interviews, Users 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 Expose CRM objects as Postgres tables the Heroku application can query and join directly
  • 02 Sync Heroku Postgres into a warehouse for reporting without running ETL dynos
  • 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

Computed and operational fields flow back

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

Mirror people records into the database

Records maintained in Greenhouse land as queryable Views, Materialized Views, Schemas, Primary and Unique Keys in Postgres Heroku, 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.

What you can sync between Greenhouse and Postgres Heroku

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 Postgres Heroku objects How this pairing syncs
Scorecards Interviewer feedback and ratings tied to interviews; read-only in Harvest, exported to a warehouse for interview analytics. Sequences Generate surrogate keys for rows created by inbound syncs. Scorecards is specific to Greenhouse and Sequences to Postgres Heroku — 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. Follower Databases Heroku-managed read replicas usable as low-impact sync sources. Scheduled Interviews is specific to Greenhouse and Follower Databases to Postgres Heroku — 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. Tables Standard Postgres tables; the primary two-way sync target for app data. Users is specific to Greenhouse and Tables to Postgres Heroku — 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. Views Read-side projections exposed to outbound syncs. Departments and Offices is specific to Greenhouse and Views to Postgres Heroku — 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. Materialized Views Precomputed result sets synced outward on refresh. Candidates is specific to Greenhouse and Materialized Views to Postgres Heroku — 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. Schemas Namespaces that scope which tables a sync reads and writes. Applications is specific to Greenhouse and Schemas to Postgres Heroku — each maps to any object or custom field on the other side.

How changes propagate between Greenhouse and Postgres Heroku

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

Postgres Heroku Greenhouse Interval-based propagation

DetectionStacksync polls Postgres Heroku for changes on an incremental schedule, reading only records changed since the previous pass. Trigger-based capture or polling in most configurations.

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.
  • Postgres Heroku: No API rate limits; connection counts and performance are bounded by the Heroku Postgres plan.
What ships with Greenhouse ⇄ Postgres Heroku

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

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

Observability

Monitoring

Track your Greenhouse ⇄ Postgres Heroku 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 Postgres Heroku.

How the Greenhouse and Postgres Heroku 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

Postgres Heroku

Integration surface
SQL wire protocol (standard PostgreSQL)
Authentication
Database credentials from the Heroku DATABASE_URL config var; SSL required
Change detection
Trigger-based capture or polling in most configurations; log-based logical replication availability depends on plan and Heroku's managed server settings
Capabilities
read · write
Rate limits
No API rate limits; connection counts and performance are bounded by the Heroku Postgres plan
How it works

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

    Choose tables

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

Greenhouse and Postgres Heroku 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 448 integrations available for Greenhouse and Postgres Heroku.

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