SVP, Development and Enterprise Architecture

eMoney Advisor, LLC

About The Position

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.

Requirements

Bachelor’s degree in computer science or related field (advanced degree preferred).
15+ years of experience leading large-scale engineering and architecture organizations.
Proven track record of delivering complex, scalable, enterprise-grade platforms in a regulated environment (financial services preferred).
Deep expertise in modern architecture patterns (microservices, APIs, cloud-native, event-driven systems).
Hands-on experience with AI/ML technologies, including generative AI and LLM-based systems, and their application in products and engineering workflows.
Experience driving DevOps, SRE, and platform engineering transformations.
Strong understanding of data architecture, data platforms, and AI integration patterns.
Demonstrated success leading global, distributed teams and driving large-scale organizational change.
Exceptional communication skills with the ability to influence both technical and non-technical stakeholders.
Collaborative executive capable of building alignment across engineering, product, and business teams and effectively and clearly communicate software engineering concepts to technical and non-technical audiences.
Demonstrated ability to establish technical leadership in highly volatile environments for mission & revenue-critical customer-facing applications.
Vast experience with managing the development of cloud-based applications and development of APIs, web services as part of a SaaS Software product.
Deep expertise in modern data, AI/ML and cloud technologies.
Courageous leader with poise and the ability to act calmly and competently in high-pressure, high-stress situations.
Must be a critical thinker, with strong problem-solving skills, project management skills: financial/budget management, scheduling, and resource management.
A strong solution orientation with a penchant for not only identifying problems but also finding ways of solving them within typical business constraints.
Ability to lead and motivate cross-functional, interdisciplinary teams to achieve strategic goals.
Strong experience overseeing a culture of coaching, development and mentorship and communicating the importance of this in your organization is a must; Hold a growth mindset and a passion for learning.

Nice To Haves

FinTech experience is a plus, but not required

Responsibilities

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.
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.
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.
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.
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.
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.
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.
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.
Continuously seeking insight into customer needs, industry trends, and emerging technologies.
Ensuring engineering teams understand how the company’s technology creates a competitive advantage.
Partnering with Product and Strategy leaders to ensure engineering investments support winning in current and future markets.