Stop manually triaging dbt test failures and chasing engineers in Slack. Redbird AI syncs data quality issues directly into Jira, tracks model dependencies across sprints, and keeps your analytics and engineering teams aligned without the busywork.
Redbird gives your team ready-to-run workflows — just connect your accounts and go.
When a dbt data quality test fails, Redbird creates a prioritized Jira issue with full context—test name, affected models, column details, and failure threshold. Engineering gets actionable tickets instead of vague Slack messages. No more manual triage or lost context.
When analytics engineers update or create dbt models tied to product features, Redbird automatically comments on linked Jira tickets with model names, dependencies, and documentation URLs. Product and engineering teams stay informed about data readiness without checking dbt Cloud.
Redbird monitors dbt schema changes—new columns, renamed fields, deprecated models—and updates corresponding Jira stories with migration checklists. Teams ship features with data layer changes properly tracked and validated before release.
When engineers close Jira tickets related to data model changes, Redbird triggers documentation updates in dbt—adding context, updating descriptions, or flagging models for review. Your data lineage stays current with actual engineering work.
Redbird pulls dbt run history, test pass rates, and model coverage metrics, then updates dedicated Jira dashboards or comments on sprint retrospective tickets. Analytics leaders get engineering-readable reports on data quality without exporting CSVs or building custom dashboards.
When a previously failing dbt test passes after code changes, Redbird automatically closes or comments on the linked Jira ticket with run metadata and timestamps. Engineers get confirmation their fixes worked without manual validation.
No engineers, no pipelines to maintain. Redbird handles the connectivity — you focus on the outcome.
Authorize dbt and Jira with OAuth or API credentials. Redbird never stores your data — it just passes through.
Tell Redbird what to do in plain language — no SQL, no code, no configuration files required.
Redbird shows you exactly what it will do before running anything. Approve the workflow, set a schedule, and switch it on.
Workflows run on your schedule or on triggers. Every run is logged. Adjust with natural language at any time.
Redbird AI understands dbt's semantic layer—models, tests, sources, snapshots—and maps them intelligently to Jira's issue hierarchy, custom fields, and workflow states.
Redbird parses dbt manifest files to understand model lineage, test configurations, and column-level metadata, then maps these to Jira issue types, priority schemes, and sprint structures. It recognizes when a failing test affects downstream dashboards and escalates the right ticket to the right team. When schema changes land in production, Redbird knows which epics and user stories need updating—no manual mapping required.
faster data quality incident response vs Slack threads and manual ticket creation
Redbird can pull from dbt and Jira simultaneously, merge the results, and format a polished report — sent on a schedule or on demand.
Set conditions in natural language. Get notified in Slack or email the moment a threshold is crossed in either dbt or Jira.
SOC 2 Type II certified. Data flows encrypted in transit and at rest. Fine-grained permission controls with full audit logs.
Push data from dbt into Jira, or from Jira back into dbt. Resolve conflicts with configurable merge rules.
Every workflow run is logged — what ran, what changed, and why. Replay or revert any individual step at any time.
Start automations from any dbt run event or Jira workflow transition—Redbird handles the rest.
Fires when any data quality test returns errors or warnings in a dbt run.
Triggers when a specific model or set of models finishes building successfully.
Activates when column additions, deletions, or type changes occur in dbt models.
Apply or update tags on specific models based on Jira workflow states or labels.
Modify dbt model documentation with context from Jira ticket resolutions or comments.
Initiate dbt runs for specific models or tests after engineering deploys linked code changes.
Fires when a Jira ticket moves to a new status—in progress, code review, done.
Activates at sprint boundaries to trigger data quality checks or reporting workflows.
Triggers when data-related Jira issues are assigned to analytics or engineering team members.
Generate new tickets with dbt test failures, model metadata, and affected downstream dependencies.
Post dbt run results, schema change summaries, or lineage updates directly to existing issues.
Write dbt-specific metadata—model names, test types, run IDs—into Jira custom fields for tracking.
Join analytics and engineering teams using Redbird AI to sync dbt and Jira—turning data quality issues into tracked, prioritized work without the manual overhead.