Lead Architect, Data Context Layer

Merck • North Wales, PA

4d • $142,400 - $224,100 • Hybrid

About The Position

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 to publish, 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 with ontologies, OWL, metadata-driven architectures, agentic AI, prompt engineering, and context engineering is required, as the platform must support both human and AI-driven consumers of context.

Requirements

• 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 with ontologies, 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 engineering concepts.
• Experience defining and enforcing technical standards across multiple teams.
• Strong ability to communicate architecture decisions to both technical and non-technical stakeholders.

Nice To Haves

• 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.

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 supports agentic AI workflows, including structured prompt context, tool access, and reliable data grounding.

Benefits

• medical, dental, vision healthcare and other insurance benefits (for employee and family)
• retirement benefits, including 401(k)
• paid holidays
• vacation
• compassionate and sick days

What This Job Offers

• Job Type: Full-time
• Career Level: Senior