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Database ⇄ Human resources

AWS Aurora MySQL to Lever integration — real-time, two-way sync

Keep AWS Aurora MySQL and Lever 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 AWS Aurora MySQL and Lever

Put your workforce data where your apps can reach it: AWS Aurora MySQL and Lever share the same people, positions, and org structure in real time.

Lever is the system of record for the people side of the business — employees, candidates, roles, and the org structure around them. AWS Aurora MySQL is where internal tools, dashboards, provisioning jobs, and analytics actually read and store records. The overlap is the workforce itself: Requisitions, Offers, Users, Stages in Lever need to exist as queryable Views, Foreign keys, Stored procedures and triggers, Databases (schemas) in AWS Aurora MySQL 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, Foreign keys, Stored procedures and triggers, Databases (schemas) in AWS Aurora MySQL with Requisitions, Offers, Users, Stages in Lever field by field, in real time. You decide which system owns which fields — Lever 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 Sync a production Aurora cluster with an analytics database while filtering out sensitive columns.
  • 02 Let operations teams edit records in a spreadsheet-style tool with changes written back to Aurora safely.
  • 03 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.
  • 04 Push Postings and Requisitions into an HRIS or warehouse to reconcile approved headcount against open roles.

Common sync patterns

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

Requisitions, Offers, Users, Stages replicate into AWS Aurora MySQL 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 Lever stay consistent in AWS Aurora MySQL, so hierarchy-driven logic and permissions don't drift.

What you can sync between AWS Aurora MySQL and Lever

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.

AWS Aurora MySQL objects Lever objects How this pairing syncs
Rows Inserted, updated, and deleted individually or in bulk during two-way syncs. Opportunities The core pipeline record for a candidate applying to a role; replaced the deprecated Candidates endpoint. Created via POST /opportunities and updated (stage, archive, links, tags, sources, files) through the API, and synced two-way with a database or HRIS. Rows is specific to AWS Aurora MySQL and Opportunities to Lever — each maps to any object or custom field on the other side.
Columns MySQL data types are mapped to the paired system's field types during schema setup. Postings Job posting records with categories, apply URLs, workplace type, and requisition codes. Can be created via POST /postings and read into a warehouse for open-role reporting. Columns is specific to AWS Aurora MySQL and Postings to Lever — each maps to any object or custom field on the other side.
Primary keys and indexes Used to match rows across systems and keep incremental syncs efficient. 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. Primary keys and indexes is specific to AWS Aurora MySQL and Requisitions to Lever — each maps to any object or custom field on the other side.
Views Can serve as read-only sync sources for derived or filtered datasets. 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. Views is specific to AWS Aurora MySQL and Offers to Lever — each maps to any object or custom field on the other side.
Foreign keys Express relationships that syncs preserve when mapping to related objects elsewhere. Users Lever team members (recruiters, hiring managers) with configurable roles; can be created via POST /users, deactivated, and reactivated through the API. Foreign keys is specific to AWS Aurora MySQL and Users to Lever — each maps to any object or custom field on the other side.
Stored procedures and triggers Existing database logic keeps firing on rows written by a sync. Stages Pipeline stage definitions that Opportunities move through; read to model funnel state and stage transitions in a database. Stored procedures and triggers is specific to AWS Aurora MySQL and Stages to Lever — each maps to any object or custom field on the other side.

How changes propagate between AWS Aurora MySQL and Lever

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.

AWS Aurora MySQL Lever Sub-second propagation

DetectionChanges in AWS Aurora MySQL are captured at the source via change data capture — no polling loop against its API. Log-based CDC via the MySQL binary log (binlog), with polling on timestamp columns as a fallback.

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

Lever AWS Aurora MySQL 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 AWS Aurora MySQL as a row-level write, with types converted between the two schemas.

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.
What ships with AWS Aurora MySQL ⇄ Lever

Connect AWS Aurora MySQL and Lever for flexible, real-time data sync.

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever AWS Aurora MySQL or Lever 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 AWS Aurora MySQL or Lever record.

Observability

Monitoring

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

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between AWS Aurora MySQL and Lever.

How the AWS Aurora MySQL and Lever connectors work

AWS Aurora MySQL

Integration surface
SQL wire protocol (MySQL-compatible), standard MySQL drivers and JDBC
Authentication
Database credentials, optionally AWS IAM database authentication, over TLS
Change detection
Log-based CDC via the MySQL binary log (binlog), with polling on timestamp columns as a fallback
Capabilities
read · write · CDC

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
How it works

How to connect AWS Aurora MySQL to Lever — 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 AWS Aurora MySQL and Lever 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
    AWS Aurora MySQL connected
    Lever connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the AWS Aurora MySQL and Lever 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 · AWS Aurora MySQL ⇄ Lever
    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
    AWS Aurora MySQL Lever
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

AWS Aurora MySQL and Lever 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 450 integrations available for AWS Aurora MySQL and Lever.

Popular · 8 of 450
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