HR integration guide
Lever + UbiGrowth workflows
Lever is an applicant tracking and recruiting platform owning candidates and interview process. This guide covers the records that matter, how the connection should be scoped, and what the first bounded workflow should be.
Introduction
Make Lever part of the workflow, not another silo.
Validate connector availability for your workspace
This guide covers how a team designs a HR workflow around Lever with UbiGrowth: which records stay authoritative, how the connection should be scoped, what the first bounded workflow should be, and how to tell whether it worked.
The records that matter are Opportunity, Candidate, Posting, Stage, Interview, Feedback, and Offer. Lever's primary object is the Opportunity — a candidate's interest in a specific role — so the same person pursuing two roles is two Opportunities against one Contact. Reporting on candidates and on opportunities counts differently.
Lever is not currently on UbiVibe's verified connector list. This page is an implementation design reference: use it to specify the workflow, then validate whether the connection is available and correctly scoped for your workspace before you make it a dependency. The verified UbiVibe connections today are Salesforce, HubSpot, Gmail, Google Drive, Slack, and GitHub.
The platform layer is the usual destination for this connection, because the value shows up as governed context and execution shared across more than one team.
Why teams evaluate this connection
Integrations create value when they remove operating friction.
The first design decision is not which API endpoint to call; it is which system owns the record, what event should trigger work, who owns the exception path, and what successful completion means.
HR systems hold the most sensitive data in the company and are usually the least connected, for good reason. Anything reading Lever is reading personal data, and the default answer to a casual integration request should be no.
That caution has a cost, though. People-operations work is full of repetitive coordination — onboarding checklists, access requests, candidate scheduling, document collection — that stays manual because the systems that could automate it are the ones nobody wants to connect broadly.
You're likely here because
- Onboarding and offboarding run on a manual checklist
- Candidate or employee coordination consumes disproportionate time
- People data cannot be connected without a privacy review nobody has scheduled
Record model
What a Lever integration actually reads and writes.
Integration design starts from the objects the system really exposes, not from a generic connector diagram. These are Lever's.
Identity and matching
Lever's primary object is the Opportunity — a candidate's interest in a specific role — so the same person pursuing two roles is two Opportunities against one Contact. Reporting on candidates and on opportunities counts differently.
Start here
Read Opportunities where Feedback is outstanding past the agreed turnaround and route the reminder to the named interviewer, with the candidate and stage stated so the reminder is actionable.
What this will not do
It will not evaluate candidates. Feedback and Scorecards carry human judgment; automation makes sure that judgment gets recorded and read.
The constraint to plan around
Lever's API is rate-limited and its webhooks fire on stage change rather than on every edit, so a workflow needing edit-level fidelity has to poll.
Build notes
What you actually have to reason about in Lever.
The fields that carry meaning, how the connection authenticates, and whether the event surface can be trusted. This is the part that decides whether the integration works in month three.
| Field | Why it matters |
|---|---|
| opportunity vs contact | the same person pursuing two roles is two Opportunities against one Contact |
| stage | pipeline position, configured per account |
| feedback / interview state | outstanding feedback is usually the actual bottleneck |
| archive reason | the loss dimension for funnel analysis |
| owner / followers | the routing targets for a reminder |
Authentication
API key over Basic auth, or OAuth for apps, with sandbox and production separated. Keys are account-wide, so scope is a matter of discipline rather than enforcement.
Events and delivery
Webhooks fire on stage change and archive rather than on every edit, so anything needing edit-level fidelity has to poll.
Workflow
How the Lever workflow runs.
The operating sequence, from reading the source system through to the result landing back where it belongs.
Step 01
Complete the privacy review
The data-protection assessment happens before the connection exists, and defines what is in and out of scope.
Step 02
Connect at the narrowest scope
Only the Lever fields the named workflow requires are connected, with sensitive categories excluded by explicit decision.
Step 03
Coordinate, do not decide
The workflow tracks tasks, sends reminders, and schedules, while every decision affecting an individual stays with a person.
Step 04
Surface status without exposing detail
Progress is visible to the people who need it without opening the underlying personal records more widely than the workflow requires.
Design decisions
The HR decisions this connection forces.
Each of these has to be settled before the Lever workflow is allowed to write anything.
Step 01
Scope to one workflow, not to the system
Connect Lever for a specific workflow with the narrowest access that supports it. Employee data should never be connected broadly because it might be useful later.
Step 02
Exclude sensitive categories deliberately
Compensation, health, performance, and disciplinary data should be excluded by explicit decision rather than by assuming a scope will not reach them.
Implementation path
How to implement the Lever workflow.
- 01
Complete the privacy and data-protection review before connecting anything; this is a prerequisite, not a follow-up task.
- 02
Pick one workflow — coordination, scheduling, or status — and scope the Lever connection to exactly what it needs.
- 03
Document which data categories are explicitly excluded and confirm the scope cannot reach them.
- 04
After feedback chasing works, add source-effectiveness reporting so recruiting spend follows the channels that actually produce hires.
Governance
Controls that matter.
Control 01
Employment decisions stay with people. Automation coordinates and prepares; it does not evaluate or decide.
Control 02
Access is scoped to a named workflow, reviewed on a schedule, and revoked when the workflow ends.
Control 03
Compensation, health, performance, and disciplinary data are excluded unless there is a specific, reviewed reason to include them.
Failure modes
How a Lever integration breaks in production.
Not generic integration advice. These follow from how this system actually behaves, which is why they look nothing like the list on the next guide over.
Symptom 01
Candidate counts differ from opportunity counts and both are quoted as truth.
Cause
One person pursuing two roles is two Opportunities.
Fix
State the unit on every metric.
Symptom 02
A candidate is archived by automation and the recruiter objects.
Cause
Archiving ends consideration and there is no meaningful undo for the candidate.
Fix
Never archive automatically; surface the candidate for a human decision.
Symptom 03
Edits made in the interface do not reach the integration.
Cause
Webhooks fire on stage change and archive, not on every edit.
Fix
Poll for edit-level fidelity where the workflow needs it.
What changes at scale
Rate limits are per key and adequate for hiring volume. The main design decision is webhook versus polling given the event coverage.
Examples
What a working Lever workflow looks like.
Bounded scenarios rather than a feature list. Each one can be verified against work the team already does.
Recruiting workflows
With Lever connected at a narrow scope, a coordination workflow can run from real records instead of a manually-maintained checklist.
Onboarding coordination
Tasks, access requests, and document collection are tracked as records with owners and status, so the checklist is not held in one person's head.
Limitations and considerations
What to validate before you depend on this.
- Lever's API is rate-limited and its webhooks fire on stage change rather than on every edit, so a workflow needing edit-level fidelity has to poll.
- Advancing or archiving candidates programmatically affects real applicants. Archiving in particular ends someone's consideration, and there is no meaningful undo in the candidate's experience.
- When candidate evaluation is the target. Feedback carries human judgment; automation should ensure it gets recorded and read, not replace it.
- Lever holds personal data. Connecting it requires a privacy and data-protection assessment before, not after, implementation.
- Employment decisions are legally regulated in most jurisdictions. Automated assistance must keep the human decision explicit and documented.
FAQ
Lever integration questions.
What records does a Lever integration actually work with?
The primary records are Opportunity, Candidate, Posting, Stage, Interview, Feedback, and Offer. Lever's primary object is the Opportunity — a candidate's interest in a specific role — so the same person pursuing two roles is two Opportunities against one Contact. Reporting on candidates and on opportunities counts differently.
What should the first Lever workflow be?
Read Opportunities where Feedback is outstanding past the agreed turnaround and route the reminder to the named interviewer, with the candidate and stage stated so the reminder is actionable.
What will a Lever integration not do?
It will not evaluate candidates. Feedback and Scorecards carry human judgment; automation makes sure that judgment gets recorded and read.
What is the main constraint to plan around?
Lever's API is rate-limited and its webhooks fire on stage change rather than on every edit, so a workflow needing edit-level fidelity has to poll.
What changes about a Lever integration at scale?
Rate limits are per key and adequate for hiring volume. The main design decision is webhook versus polling given the event coverage.
How does authentication work for Lever?
API key over Basic auth, or OAuth for apps, with sandbox and production separated. Keys are account-wide, so scope is a matter of discipline rather than enforcement.
Does Lever support webhooks, and can they be trusted?
Webhooks fire on stage change and archive rather than on every edit, so anything needing edit-level fidelity has to poll.
What is the risk of writing to Lever?
Advancing or archiving candidates programmatically affects real applicants. Archiving in particular ends someone's consideration, and there is no meaningful undo in the candidate's experience.
When is connecting Lever the wrong call?
When candidate evaluation is the target. Feedback carries human judgment; automation should ensure it gets recorded and read, not replace it.
What should a Lever integration automate first?
Start with one bounded workflow that removes a measurable handoff, duplicate-entry step, reporting delay, or follow-up gap. Expand only after the first workflow is reliable.
Does UbiGrowth require Lever to be replaced?
No. The operating model is designed around connecting to systems that should remain authoritative and building workflows around them rather than forcing a wholesale replacement.
Is connector availability identical for every workspace?
No. Availability can depend on provider configuration, authentication, scopes, workspace setup, and deployment state. Validate the required connection before treating it as an operational dependency.
Is it safe to connect Lever?
Only at a narrow, reviewed scope for a specific workflow, after your own privacy assessment. Employee data should not be connected broadly on the chance it becomes useful.
Can this screen or evaluate people?
No. Employment decisions stay with people. These workflows coordinate, schedule, and track — they do not evaluate individuals.
What data should be excluded?
Compensation, health, performance, and disciplinary data unless there is a specific, reviewed reason to include it. Exclusion should be an explicit decision.
How this access is governed
What ARIA is allowed to do in Lever, and who decides.
Connecting Lever is a permission decision, not just a setup step. These are the controls that decide what ARIA can reach, what it can change, what gets recorded, and how you take the access back.
Required permissions
ARIA works through the scopes the connection was granted, and no others. Authorization happens at the provider, so the permissions being requested are shown by the system itself before anything is connected.
What it can reach
Reachable systems are the intersection of what your organization approved in the connector registry and what the requesting identity is permitted to use. Identity resolves before execution, not after.
What it can do
Actions run through explicit execution paths with state, spend, and failure boundaries — a bounded worker path rather than an open-ended agent loop with a credential.
Credential handling
Credentials live in the governed connection layer and are resolved through canonical connection identity. They are not pasted into individual workflows, prompts, or generated artifacts.
Action logging
Execution carries state and traces: what triggered the work, which connection it used, and what came back — including an explicit failure when something did not run.
Approval and revocation
Consequential actions can be made to require a person to approve them. Access can be changed or revoked at the connection, and ARIA loses that reach without unpicking the work already completed.
Start with ARIA
Ask ARIA to run this integration.
Describe the outcome you need across this system. ARIA works out the scopes, data, and actions the job requires, and operates inside the access you grant — which you can change or revoke.
- ARIA acts only through the systems and permissions you connect.
- Connections use scoped credentials you can change or revoke.
- Actions are recorded, and consequential ones can require approval.
Start here
Turn the integration into a working business outcome.
Start with ARIA to describe the outcome, then continue into the product path that fits the workflow. Connector availability and required scopes should be validated for the specific workspace before production use.