for Collibra, managing its configuration, upgrades, and performance independently.
Architect and implement
enterprise-wide data governance solutions
using Collibra, aligned with business and regulatory needs.
Design and develop
custom workflows (BPMN)
, integrations (REST/SOAP APIs), and extensions within Collibra.
Lead the
end-to-end implementation
of metadata management, data cataloging, and lineage tracking using Collibra.
Collaborate with enterprise architects, data engineers, and business stakeholders to integrate Collibra with platforms like
Azure, Databricks
,
Snowflake
and BI tools.
Define and enforce
Collibra operating model
, including roles, responsibilities, stewardship, and governance workflows. Define and evolve the asset model for business use cases.
Provide
expert-level support and troubleshooting
for Collibra users and integrations.
Conduct advanced
training sessions
for technical teams and business users.
Evaluate and roll out
new product features
for business needs.
Drive
continuous improvement
and innovation in Collibra usage across the organization.
Ensure Collibra supports
regulatory compliance
and audit requirements through robust lineage and traceability.
Required Qualifications & Skills:
7+ years
of experience in data governance, metadata management, or related fields.
Extensive hands-on experience
with Collibra Data Intelligence Platform in a large enterprise environment.
Proven ability to
manage Collibra independently
, including configuration, workflow development, and integration.
Deep understanding of
Collibra's operating model
, technical architecture, and customization capabilities.
Strong experience with
REST/SOAP APIs
and BPMN workflows.
Expertise in
data cataloging, lineage, metadata standards
, and governance frameworks.
Experience integrating Collibra with
cloud data platforms
and enterprise systems.
Excellent problem-solving, communication, and stakeholder management skills.
Preferred Qualifications:
Collibra Ranger or Architect certification
(highly desirable).
Experience in regulated industries such as
life sciences or pharmaceuticals
.
Background in
data engineering, data architecture
, or enterprise data platforms.
* Familiarity with
data quality tools
and frameworks.
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