Collibra Data Governance Engineer

Job Title: Collibra Data Governance Engineer

Work Location & Reporting Address: Bellevue, WA 98006 (Onsite-Hybrid. Will consider candidates willing to relocate to client’s location)

Mode of interview: Teams Video
Long term contract

Must Have Skills:
⦁ Collibra DGC
⦁ Collibra Workflow (BPMN)
⦁ Collibra APIs

Detailed Job Description:
Key Responsibilities (Engineering‐Focused)
API, Integration & Automation
⦁ Design and implement API‐driven integrations to ingest abandoned, duplicate, archival, and sensitive data findings from Sec.ai and Databricks into Collibra, supporting pagination, retries, error handling, and idempotent processing.
⦁ Define and manage versioned JSON schemas for findings, exceptions, evidence, ownership, and remediation status, including timestamp‐driven UPSERT logic and conflict resolution.
⦁ Automate bi‐directional status synchronization across source systems, Collibra, and BI layers using APIs, webhooks, and scheduled jobs.

Metadata Modeling & Governance (Collibra)
⦁ Extend the Collibra operating model with custom asset types, attributes, relations, and controlled vocabularies for data minimization strategies (Delete, Archive, Partial, Valid Retention, Exceptions).
⦁ Implement metadata validation rules, required fields, enumerations, RBAC enforcement, and segregation of duties for owners, stewards, and custodians.
⦁ Model lineage, ownership, and stewardship across cloud and NAS assets, ensuring accurate mapping between datasets, applications, and platforms.

Workflow Engineering & Exception Management
⦁ Build and maintain BPMN‐based workflows in Collibra for remediation, exception governance, approvals, and Director‐level escalations.
⦁ Implement workflow resets, state transitions, SLA timers, and audit trails to support governance and compliance requirements.
⦁ Operationalize exception workflows, including justification capture, approval/rejection logic, routing rules, and SLA enforcement.

Remediation Evidence & Compliance
⦁ Design evidence‐based remediation workflows, supporting secure proof‐of‐remediation uploads, validation fields, analyst fallback mechanisms, and auditability.
⦁ Ensure remediation evidence aligns with privacy, legal, and governance standards, including retention and access controls.

Data Quality, Status Mapping & Alignment
⦁ Define a canonical remediation status model and map cross‐platform statuses from Sec.ai and Databricks.
⦁ Implement data‐quality checks (completeness, consistency, timeliness) to ensure accurate remediation tracking and reporting.

Reporting, Analytics & Dashboards
⦁ Define technical reporting requirements and KPI semantics for:
1. Abandoned and duplicate data volumes
2. Remediation progress and aging
3. Exception trends
4. Evidence compliance
5. SLA performance
⦁ Define detailed sensitive data reporting requirements and collaborate with Databricks and BI teams to deliver executive‐level dashboards.

Testing, Validation & Delivery
⦁ Lead UAT and cross‐functional validation with governance, legal, data engineering, security, and cloud platform teams.
⦁ Ensure workflows, integrations, and reports meet functional, compliance, scalability, and usability standards.