SVP, Development and Enterprise Architecture

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The SVP of Development and Enterprise Architecture leads the evolution of eMoney Advisor's engineering organization into an AI-first, platform-driven, and product-centric ecosystem. This leader is responsible for redefining how software is built, delivered, and operated by embedding AI, automation, and data intelligence across the entire software development lifecycle. This role leads both Development and Architecture, partnering closely with Product Leadership and other Business leaders to deliver differentiated wealth management and financial wellness solutions, ensuring engineering teams understand what, why, and the expected outcomes of every initiative. Ensure development leaders can clearly articulate the customer problem being solved, the strategic rationale, and the business benefits of their work. Foster a culture where engineers actively participate in solution design and innovation, not simply execution. The SVP will build and scale high-quality, intelligent platforms, systems, and services that are extensible, resilient, secure, and continuously evolving. As a key member of the Technology leadership team reporting to the Head of Technology, this role is accountable for driving engineering strategy, accelerating delivery through AI-enabled practices, and creating sustainable competitive advantage through technology innovation. Architecture & Platform Strategy Define and evolve the enterprise architecture vision across application, data, and AI layers, aligned to business strategy. Lead the transition to a platform engineering model enabling self-service, reusable capabilities, and developer acceleration. Architect systems that are cloud-native, API-first, event-driven, and designed for continuous change and scale. Establish patterns for AI integration, including model orchestration, feature reuse, and real-time decisioning. AI-First Engineering Transformation Define and execute an AI-first engineering strategy that embeds AI agents, and automation across the SDLC (design, coding, testing, deployment, and operations). Drive adoption of generative AI and machine learning to improve developer productivity, code quality, and delivery velocity. Establish governance, security, and risk management practices for responsible AI usage across engineering. Product Engineering & Delivery Excellence Lead global engineering teams to deliver high-quality products and enhancements with speed, predictability, and quality. Modernize development practices through AI-assisted engineering, CI/CD, trunk-based development, and infrastructure as code. Own and improve key engineering metrics (e.g., DORA metrics, developer experience, cycle time, defect rates). Partner with Product to align delivery with business priorities, driving measurable customer and business outcomes. Data & AI Integration Partner with Product leadership to embed AI/ML capabilities directly into core products and workflows. Enable scalable data platforms and pipelines that support real-time insights and intelligent experiences. Promote reuse of data, models, and AI services across the organization to maximize leverage and consistency. Engineering Excellence, Quality & Security Champion a "built-in quality" mindset with automation, AI-driven testing, and shift-left practices. Partner with QA leadership to evolve testing into an AI-enabled, continuous quality engineering discipline. Partnering with Information Security lead secure-by-design practices and application security modernization, including proactive risk detection and remediation. Organizational Leadership & Talent Development Build, lead, and inspire a high-performing global organization of engineers and architects. Upskill teams on AI-native development, modern architecture, and platform engineering practices. Foster a culture of innovation, experimentation, accountability, and continuous learning. Align organizational structure and talent to evolving business and technology priorities. Strategy, Innovation & Thought Leadership Act as a thought leader on emerging technologies, particularly AI/ML, and their application to wealth management. Continuously assess industry trends to inform platform evolution and competitive differentiation. Influence executive stakeholders by translating technology strategy into business value and outcomes. Continually drive modernization of the existing technology base across cloud, data, AI, and distributed systems. Governance & Operational Excellence Help establish lean governance, standards, and architectural guardrails to ensure consistency without slowing innovation. Help oversee budgets, resource allocation, and global delivery models to optimize efficiency and effectiveness. Ensure compliance with enterprise architecture standards, regulatory requirements, and security best practices. Customer, Market, and Competitive Awareness Continuously seeking insight into customer needs, industry trends, and emerging techn

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