Responsibilities:

Conduct a comprehensive assessment of the current CMDB architecture, identify data gaps, and define a roadmap to a modern, AI-ready CMDB ecosystem.
Design and implement a target-state architecture that integrates ServiceNow with Databricks and Microsoft Fabric, enabling gold-tier datasets to flow into the CMDB from diverse source systems.
Modernize CI lifecycle management, reconciliation, and data quality processes through automated, AI-assisted, and data-driven workflows.
Develop and maintain ServiceNow modules supporting ITSM and CMDB governance, including workflow automation, business rules, and module configuration.
Reduce dependencies on IT teams by creating data-driven solutions to automate relationship mapping, dependency integrity, and reconciliation.
Introduce AI/ML capabilities such as knowledge graphs and predictive intelligence to surface relationships between assets and proactively identify dependencies.
Design and oversee CMDB audits, KPI dashboards, and continuous improvement processes to maintain data quality and integrity.
Leverage Azure cloud services and Databricks for scalable data ingestion, transformation, and processing.
Implement automated job scheduling and observability with self-healing AIOps capabilities.
Serve as the technical lead and mentor for the CMDB team, establishing best practices, coding standards, and architectural principles.
Ultimately accountable for the Quality and Integrity of the CMDB.
Manage a team of passionate CMDB data engineers.

What We're Looking For:

Key qualifications:

Experience: 5-10 years in data engineering and or CMDB management and architecture.
Education: Bachelor's degree in Computer Science, Engineering, Information Systems, or a related field.
CMDB Expertise: Deep understanding of CMDB concepts including CI lifecycle management, data model governance, relationship mapping, reconciliation, audits, and KPI dashboards.
ServiceNow Development: Hands-on experience with workflow automation, business rules, module configuration, discovery, and integration with enterprise systems.

Data Engineering: Strong skills in ETL/ELT pipeline design, Python for data transformation, Databricks for scalable processing, and Microsoft Fabric for data lakehouse architectures and data distribution.

AI/ML CMDB Solution Architect