Skip to content
Human resources ⇄ Data warehouse

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

Keep Lever 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.

  • 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

Case study
Migrated from MuleSoft
Case study
Migrated from Celigo
Migrated from Heroku Connect
Migrated from Matillion
Case study
Migrated from Fivetran
Case study
Migrated from Celigo
Why teams connect Lever and Snowflake

Land the people and organization records from Lever in Snowflake as live tables for workforce reporting, without extract jobs, and write computed results back where Lever 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 Lever 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 Feedback, Interviews, Notes and Contacts, Opportunities from Lever 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 Lever 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 Feed finance reconciliation models from ERP data landed in Snowflake on a continuous basis
  • 02 Land CRM and ERP records in Snowflake continuously so BI reflects business systems without nightly batch ETL
  • 03 Write enriched Sources, Tags, and Contact data back onto Opportunities from an outbound sourcing pipeline.
  • 04 Two-way sync Opportunities and their Stages between Lever and a Postgres database so recruiting ops and analysts query the pipeline in SQL while recruiters keep working in Lever.

Common sync patterns

Queryable history for planning and audit

A continuously synced copy in Snowflake gives you a durable, queryable record of how Lever's records change over time, for headcount planning and audit questions.

Write-back of computed values

Segments, rollups, or risk flags computed in Snowflake sync back onto the matching records in Lever, where the HR team sees them in the system they already use.

HR data in the warehouse, minus the pipeline

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

What you can sync between Lever 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.

Lever objects Snowflake objects How this pairing syncs
Stages Pipeline stage definitions that Opportunities move through; read to model funnel state and stage transitions in a database. Stages File staging areas used for bulk loads into synced tables. Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions.
Requisitions Headcount/requisition records with custom requisition fields, tied to Postings; read via GET /requisitions and synced to an HRIS to reconcile approved headcount against open roles. Tasks Scheduled SQL used to transform synced data after it lands. Requisitions is specific to Lever and Tasks to Snowflake — each maps to any object or custom field on the other side.
Offers Offer records attached to an Opportunity with status and offer-form fields; exposed read-only through GET /opportunities/:id/offers, so they sync outbound to an HRIS or onboarding system when a candidate reaches the offer stage. VARIANT Columns Semi-structured JSON payloads stored alongside relational columns. Offers is specific to Lever and VARIANT Columns to Snowflake — each maps to any object or custom field on the other side.
Users Lever team members (recruiters, hiring managers) with configurable roles; can be created via POST /users, deactivated, and reactivated through the API. Virtual Warehouses The compute a sync's queries run on, sized independently of storage. Users is specific to Lever and Virtual Warehouses to Snowflake — each maps to any object or custom field on the other side.
Feedback Interview feedback and scorecard forms attached to Opportunities; created via POST /opportunities/:id/feedback and consolidated into a warehouse for interviewer analytics. Databases Top-level containers that scope which data a sync can touch. Feedback is specific to Lever and Databases to Snowflake — each maps to any object or custom field on the other side.
Interviews Scheduled interview panel events with times and interviewers; read for scheduling reporting and time-to-hire metrics, and creatable via the panels endpoint. Schemas Namespaces within a database used to organize synced tables. Interviews is specific to Lever and Schemas to Snowflake — each maps to any object or custom field on the other side.

How changes propagate between Lever 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.

Lever Snowflake Sub-second propagation

DetectionLever notifies Stacksync of record changes through webhook events. Webhooks for candidate and application lifecycle events (applicationCreated, candidateStageChange, candidateArchiveStateChange, candidateHired,.

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

Snowflake Lever 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 Lever through its API, with automatic retries and rate-limit backoff.

Rate-limit considerations

  • Lever: 10 requests/second per API key with a token-bucket burst to ~20/s; POSTs that create candidates/applications are throttled to roughly 2/second. Sustained overage returns 429 with Retry-After. List endpoints are cursor-paginated at up to 100 records per page.
  • Snowflake: No conventional API rate limits; cost and throughput are governed by virtual warehouse size and running time.
What ships with Lever ⇄ Snowflake

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Lever 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 Lever or Snowflake record.

Observability

Monitoring

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

Trading partners

EDI

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

How the Lever and Snowflake connectors work

Lever

Integration surface
REST Data API (api.lever.co/v1)
Authentication
API key over HTTP Basic auth (key as username, blank password) for internal integrations, or OAuth 2.0 with 1-hour access tokens for partner integrations (auth.lever.co)
Change detection
Webhooks for candidate and application lifecycle events (applicationCreated, candidateStageChange, candidateArchiveStateChange, candidateHired, interview created/updated/deleted), plus incremental polling via created_at and updated_at range filters on Opportunities
Capabilities
read · write · webhooks
Rate limits
10 requests/second per API key with a token-bucket burst to ~20/s; POSTs that create candidates/applications are throttled to roughly 2/second. Sustained overage returns 429 with Retry-After. List endpoints are cursor-paginated at up to 100 records per page.
Lever 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 Lever 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 Lever 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
    Lever connected
    Snowflake connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Lever 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 · Lever ⇄ 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
    Lever Snowflake
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
Try interactive demo
FAQ

Lever 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.

Learn more about security
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:

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 Lever and Snowflake.

Popular · 7 of 547
All Lever integrations All Snowflake integrations Browse all integrations
Coworkers laughing in front of a laptop in a casual office setting

Your last integration took months.
Your next one takes a prompt.

Book a demo Tour the platform on your own