Data Capability Engineer

Job title: Data Capability Engineer

Location: Remote

Reports to: IT Systems Architecture

Contract: Permanent, Full-time

Purpose of the Role:

Own and extend the accuracy, completeness, and usability of the service management platform's data — so implementation teams and AI-driven capabilities can act on it directly, and so priority consumers like vulnerability and exposure management run on complete, trustworthy ground truth. Service mapping and asset data are the anchor domain and immediate priority; the durable mandate is data quality across an increasingly AI-centric ITSM platform, where automated systems act on data without a human to catch errors first. A data-capability role, not a security role.

Core responsibilities — Anchor domain: Service & asset data (immediate priority) • Ensure existing service mapping is accurate and current across hosts, servers, and applications. • Extend mapping coverage to data types not well covered today — certificates, websites, agents, and SaaS applications. • Engage in the workflows that onboard new applications and capabilities into the CMDB so coverage keeps pace rather than drifting. • Go deeper into discovery and software-asset tooling — Lansweeper, the software asset management system, and agent-based discovery — to raise inventory fidelity.

Core responsibilities — Broader mandate: Platform & AI-consumption data quality • Apply the same discipline to other high-value platform data — service catalog and metadata, ticket routing and assignment-group data, and the Dataverse structures underneath delivery capabilities such as the AskIT service chatbot. • Raise data to the standard AI systems require: complete, disambiguated, and semantically clean, so intent classification, catalog matching, and automated routing act on trustworthy inputs rather than amplifying noise. • Leverage the existing foundation (Dataverse schema, discovery tooling, search indexes already in place) rather than building net-new infrastructure. • Make data consumable without re-verification - the test is whether an engineer or an automated agent can act on a record without re-checking it, and turn recurring data problems into repeatable quality practices.

What this role is not • Not remediation coordination or change management • Not a security function - it supplies the data; it does not own security decisions. • Not a net-new platform build - leverages and improves the existing foundation.

Example Success measures • Coverage: % of assets, certificates, websites, agents, and SaaS apps mapped to owner and service; extended over time to other platform data domains. • Data confidence: high enough that implementation teams and AI capabilities don't require re-verification. • Time-to-onboard a new application or capability into the CMDB. • Reusability: improvements delivered as repeatable practices, not one-off fixes.

What You’ll Need:

Education: Bachelor's degree in information systems, computer science, data management, or a related field — or equivalent practical experience. A candidate with a strong track record in CMDB, discovery, or data-quality work should not be screened out for lack of the exact degree.

Experience: Five to eight years in IT data, configuration/asset management, or a data-engineering-adjacent role, including demonstrable ownership of a data domain (not just contributing to one). Enough experience to set the standard for how data quality is done, with the judgment to work independently under a Senior Manager's direction rather than needing task-level supervision.

Required skills and knowledge Anchor domain — asset & service data • Hands-on experience with CMDB and service mapping concepts (configuration items, relationships, service dependencies), including keeping mapping accurate as the environment changes. • Working knowledge of discovery and software-asset tooling — Lansweeper, agent-based discovery, and a software asset management system — and what drives inventory fidelity in each. • Understanding of the asset types the role must extend into: certificates, websites, agents, and SaaS applications.

Broader mandate — platform & AI-consumption data quality • Strong data-quality fundamentals: profiling, deduplication, normalization, and defining and measuring completeness and accuracy. • Practical experience querying and shaping data in a structured platform (Dataverse or a comparable relational/low-code data platform), working with existing schema rather than designing greenfield systems. • Query capability sufficient to investigate, validate, and correct data at scale independently. • Familiarity with what makes data consumable by AI systems — why completeness, disambiguation, and clean semantics matter for intent classification, matching, and routing. Deep machine-learning expertise is not required; the relevant skill is data readiness, not model building.

Ways of working • Able to embed quality into existing onboarding workflows and turn recurring problems into repeatable practices, rather than one-off fixes. • Communicates data issues and their business impact clearly to both technical teams and non-technical stakeholders.

Your Advantage:

Experience in a Microsoft Power Platform environment (Power Automate, Azure AI Search, or similar).

Exposure to vulnerability or exposure management as a data consumer — understanding what that function needs from asset data, without owning the security decisions.

ITIL familiarity (service management context), or a data-management certification (e.g., DAMA/CDMP).