Lead Enterprise Architect

Job Overview

The Enterprise Architecture Lead is a senior technical leader within Merkle’s Digital Product Engineering (DPE) practice, part of the broader Digital Experience (DX) organization. DPE serves as Merkle’s rapid innovation engine and cross‑capability orchestrator, delivering bespoke product development, composable modernization, and AI‑powered solutions for Fortune 100 clients. This role combines deep enterprise architecture expertise with a product engineering mindset. The candidate must be equally comfortable governing complex, multi‑platform delivery programs and shaping what gets built and why, leading multi‑disciplinary engineering teams through the practical development and delivery of production‑grade solutions.

The Enterprise Architecture Lead operates in close partnership with DPE’s Technology Lead, who owns the agentic AI and emerging technology workstream on the account. Together they form a complementary architecture leadership pair: one focused on the foundational platform ecosystem, integration architecture, and delivery governance; the other on next‑generation AI capabilities and innovation. This collaboration model ensures the client receives both operational stability and forward‑looking technical vision from a unified DPE team.

• Architectural Governance & Delivery Oversight – Owning the end‑to‑end technical architecture across the engagement, ensuring all solution components align to the approved roadmap, adhere to enterprise standards, and are delivered with the quality and rigor expected of a world‑class digital product engineering organization.
• Multi‑Platform Solution Architecture – Designing and governing integrated, enterprise‑class solutions anchored in the Adobe ecosystem (AEM, AEP, Target, Analytics, Workfront) while orchestrating adjacent platforms (Salesforce Clouds, middleware, data infrastructure, and enterprise integration layers) into a unified, cohesive architecture. This includes defining clear architectural boundaries and integration contracts with the agentic AI workstream.
• Web & Digital Experience Delivery – Leading the initial phase of work focused on uplifting the client’s most critical digital properties, using Adobe Experience Manager as the foundational platform while ensuring the architecture is composable enough to support future modernization and AI‑powered enhancement.
• Enterprise Integration Architecture – Designing scalable integration patterns across platforms, APIs, data layers, and middleware to ensure seamless data flow, system interoperability, and operational resilience across a complex, global technology estate. This includes defining the integration surface between traditional platform services and emerging AI/agent capabilities.
• Delivery Excellence & Execution Discipline – Bringing the operational rigor and delivery methodology of a world‑class product engineering engagement: sprint‑level architecture involvement, technical debt management, dependency mapping, risk mitigation, and hands‑on leadership that keeps large, multi‑team programs on track.
• Technical Leadership & Team Orchestration – Leading and mentoring multi‑disciplinary delivery teams (developers, platform specialists, data engineers, QA) across work streams, ensuring technical alignment, quality standards, and consistent architectural decision‑making.
• Client‑Facing Technical Partnership – Serving as a senior technical authority with client stakeholders at the VP and C‑suite level, translating complex architectural decisions into business‑relevant language and building confidence in the delivery approach. Presenting a coherent technical narrative that connects platform stability with innovation velocity.
• Innovation Within Execution – Identifying and incorporating emerging capabilities (AI‑powered personalization, intelligent automation, agentic workflows) into the delivery roadmap where they add measurable value, not as theoretical exercises, but as production‑ready enhancements integrated within the existing architecture. Working with DPE’s Technology Lead to evaluate where AI capabilities should be natively embedded vs. independently orchestrated.
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