Job Description

Role Overview

This role is part of a broader enterprise initiative to establish a Data Context Layer (DCL) a foundational capability designed to provide consistent reusable and scalable context across enterprise data products.

The DCL is intended to address challenges related to data fragmentation lack of shared semantics and inconsistent interpretation of data across systems and products. It establishes a unified layer for representing context relationships and meaning enabling downstream products to operate with greater consistency interoperability and intelligence.

In addition the DCL plays a critical role in enabling agentic AI capabilities across the enterprise by providing the structured context and semantic grounding required for intelligent agents to operate reliably. This includes ensuring that agent-driven workflows and decisions are based on consistent governed and interpretable data context reducing risks associated with fragmentation ambiguity and lack of control.

Within this initiative we are seeking a Lead Architect to define and guide the conceptual and technical architecture for an enterprise platform that enables teams topublish discover and consume standardized data context through self-service. This role will shape the target architecture integration patterns and technical standards that support scalable secure and production-ready context services across the enterprise.

The ideal candidate has deep experience in enterprise architecture data platforms semantic technologies and AI-enabled systems. Familiarity withontologies OWL metadata-driven architectures agentic AI prompt engineering and context engineeringis required as the platform must support both human and AI-driven consumers of context.

Key Responsibilities

• Define the target architecture for the enterprise data context self-service platform.

• Establish architectural standards and patterns for:

  • context publishing

  • discovery

  • access control

  • versioning

  • validation

  • lineage and provenance

  • observability and monitoring

• Partner with product engineering service design security and governance teams to ensure the platform is technically sound and aligned to enterprise needs.

• Evaluate and recommend technologies and integration patterns including:

  • ontology and semantic modeling approaches

  • OWL/RDF-based systems

  • knowledge graphs

  • semantic layers

  • MCP A2A and Timbr-like technologies where applicable

• Design architecture that supports both self-service user workflows and AI/agentic consumption patterns.

• Ensure context can move from development to production in a governed scalable and supportable way.

• Define non-functional requirements including performance resilience security compliance and operational maintainability.

• Review solution designs and implementation plans to ensure adherence to architectural standards.

• Create reference architectures patterns and decision records to guide implementation teams.

• Collaborate with platform teams to support long-term scalability reuse and interoperability across domains.

• Help shape how the platform supportsagentic AI workflows including structured prompt context tool access and reliable data grounding.

Minimum Required Qualifications

• Bachelors degree

• 8 years of experience in enterprise architecture solution architecture data architecture or platform architecture.

• Strong technical background in data platforms metadata systems semantic technologies or AI infrastructure.

• Familiarity withontologies OWL RDF knowledge graphs and semantic layer concepts.

• Experience designing platforms that support self-service enterprise-scale reuse and governed access.

• Expertise in agentic AI prompt engineering and context engineeringconcepts.

• Experience defining and enforcing technical standards across multiple teams.

• Strong ability to communicate architecture decisions to both technical and non-technical stakeholders.

Preferred Qualifications

• Experience with MCP A2A Timbr or similar context-serving or semantic interoperability technologies.

• Background in enterprise data governance metadata management or data catalog architectures.

• Experience supporting AI-enabled platforms or agentic workflows.

• Knowledge of production concerns such as observability testing CI/CD identity and access management and lifecycle management.

• Experience creating reference architectures and reusable patterns for platform teams.

• Familiarity with regulated or security-sensitive enterprise environments.

What Success Looks Like

Success in this role means:

• the platform has a clear scalable Conceptual/Technical architecture

• context is served consistently and securely across the enterprise

• teams can build and integrate using reusable patterns

• development-to-production flows are reliable and governed

• the platform supports both enterprise applications and AI/agentic use cases

• architectural decisions reduce duplication and increase long-term maintainability

Required Skills:

Preferred Skills:

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The salary range for this role is

This is the lowest to highest salary we in good faith believe we would pay for this role at the time of this posting. An employees position within the salary range will be based on several factors including but not limited to relevant education qualifications certifications experience skills geographic location government requirements and business or organizational needs.

The successful candidate will be eligible for annual bonus and long-term incentive if applicable.

We offer a comprehensive package of benefits. Available benefits include medical dental vision healthcare and other insurance benefits (for employee and family) retirement benefits including 401(k) paid holidays vacation and compassionate and sick days. More information about benefits is available at can apply for this role through (or via the Workday Jobs Hub if you are a current employee). The application deadline for this position is stated on this posting.

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