Principal Information Architect – Metadata Thought Leadership

Overview

As Principal Information Architect, you drive metadata-driven engineering in a cloud-centric, enterprise setting. You bridge information architecture with software engineering to build metamodels, DSLs, and tooling that enable faster, clearer development with governance and auditability. You collaborate with the Chief Data Office, product owners, and engineering teams to standardize models and ensure regulatory alignment. You influence technical direction through thought leadership and practical implementations that scale across a large, complex environment.

Responsibilities

• Develop and maintain the metamodel to support evolving business needs
• Create metadata assets (controlled vocabulary, mappings, lineage, data content descriptions)
• Build and maintain mappings between metadata and software engineering assets
• Design and maintain domain-specific languages representing metadata
• Prototype and maintain tooling to capture, validate, transform, generate code from, report on, and present metadata
• Engage with customers and stakeholders to communicate vision and standards and support implementation
• Architect foundations that enable AI capabilities, including provenance, policy tagging, and interoperability across workflows
• Establish reuse-first, metadata-driven patterns and governance for AI-enabled platforms with traceability, resiliency, and security controls

Key requirements

• Create clear data models for technical and non-technical audiences
• Use UML tools (e.g., MagicDraw, PowerDesigner, Eclipse-based)
• Apply metamodel concepts (UML, MOF) for modeling governance
• Work with controlled vocabularies and metadata standards
• Produce/interpret mappings and lineage for data movement and transformation
• Translate models to programming/schema representations; build/maintain DSLs and ASTs
• Proficient in Java, JavaScript, Python, or C++; implement language tooling components
• Prototype solutions independently and handle ambiguous design challenges
• Collaborate with business/technical teams to translate and playback requirements
• Experience designing metadata/language architectures for AI-enabled solutions with governance

• Collaborative mindset
• Strong communication with diverse stakeholders
• Ability to navigate ambiguity and drive solutions
• UML modeling and MOF-based metamodeling
• Domain-specific languages and their abstract syntax
• Metadata standards and controlled vocabularies

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