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Mid-level metro role with explicit years and moderate brand, but specialized CMDB/discovery skills limit applicant pool.
Strong CMDB, asset-discovery, and ITSM data requirements create high domain specificity, limiting cross-industry transferability.
Explicit 5–8 year requirement plus mandatory CMDB, discovery tooling, and data-quality skills makes filters strict.
Own and continuously improve data accuracy, completeness, and usability in the service management platform, focusing immediately on service mapping and asset data domains.
Extend data coverage and fidelity across various asset types (certificates, websites, agents, SaaS applications) and embed quality practices into onboarding workflows to support AI-driven automation without manual re-verification.
Ensure platform data quality for AI consumption across service catalog, ticket routing, and metadata using existing tools and schemas to enable trustworthy, reusable data for implementation teams and automated systems.
5-8 years in IT data, configuration/asset management, or data-engineering-adjacent roles with demonstrable data domain ownership.
Hands-on experience with CMDB, service mapping concepts, discovery/software-asset tools (e.g., Lansweeper), and extending inventory coverage to diverse asset types.
Proficiency querying and shaping data in structured platforms (e.g., Dataverse or similar relational/low-code platforms) with strong data-quality fundamentals (profiling, deduplication, normalization).
Bachelor's degree in information systems, computer science, data management or equivalent practical experience.
Experienced in managing and elevating data quality specifically in IT service management platforms with AI integration, demonstrating independent judgment under senior management.
Shows deep understanding of service mapping and discovery tooling to maintain and extend accurate asset inventories aligned to business consumption needs.
Capable of embedding repeatable data quality practices within existing operational workflows and communicating complex data issues clearly across technical and non-technical stakeholders.