Enterprise Architecture Analyst

The Enterprise Architecture Analyst III supports Oura’s Enterprise Architecture function by maintaining an accurate, business-owned application and capability inventory, primarily in LeanIX. In an initial 6-month term, this role partners with business and IT stakeholders to keep applications, ownership, and metadata up to date, and delivers clear reporting that enables Profitable Growth and Operational Excellence through better application portfolio management.

• →Maintain and improve the LeanIX application and capability inventory, ensuring apps, interfaces, and business capabilities are accurately represented and kept current.
• →Partner with business owners across functions to confirm application ownership, lifecycle status, and basic application portfolio management (APM) attributes (criticality, usage, risk).
• →Build and maintain standard reports and views (e.g., in LeanIX and Lovable) that give Enterprise Architecture and business leaders visibility into the application landscape and ownership.
• →Use EA tooling (LeanIX, Jira, Confluence) and AI tools (e.g., Claude, Glean, Lovable) to streamline data collection, validation, documentation, and reporting.
• →Support EA intake and governance processes by preparing application/capability views and documentation for reviews and decision-making.

We would love to consider you for this role if you have:

• 2 + years of experience as a business systems, data, or enterprise architecture analyst working with application portfolios or system inventories in a complex environment.
• Familiarity with enterprise architecture or application portfolio tools (e.g., LeanIX or similar) and collaboration tooling (e.g., Jira, Confluence) to manage data and workflows.
• Strong attention to detail and a structured approach to keeping data accurate, consistent, and well-documented across multiple stakeholders.
• Comfortable engaging directly with business and IT partners to clarify ownership, usage, and lifecycle status for applications and capabilities.
• Clear written and verbal communication skills; able to produce simple diagrams, tables, and reports that non-technical stakeholders can quickly understand.
• Demonstrated AI fluency: regular use of LLM assistants and agents (e.g., Claude) and AI app-building tools (e.g., Lovable, Glean) for research, summarization, data quality checks, and report building, with a habit of verifying and correcting AI output before sharing.