Enterprise Architect

Blackbaud · Удалённо · United States

# Enterprise Architect **Blackbaud** · United States · `Remote` · `Full Time` 💼 **Уровень роли:** `Senior` 🕒 **Статус:** *Опубликовано: сегодня* · *Источник: Himalayas (JSON API)* --- ### Top Skills & Match 🎯 **Ключевой стек роли:** `[Enterprise-Architecture]` `[Data-Architecture]` `[AI-ML-Architecture]` `[Solutions-Architect]` `[Cloud-Architecture]` `[Enterprise-Architect]` `[Enterprise-Application-Architect]` `[Enterprise-Systems-Architect]` `[Principal-Enterprise-Architect]` `[Chief-Enterprise-Architect]` `[Enterprise-Architecture-Consultant]` `[Lead-Enterprise-Architect]` `[Enterprise-Solution-Architect]` --- ### About the Role Enterprise Architect We are seeking an experienced Enterprise Architect for Data & AI to help shape the future of our SaaS platform. Data and AI are foundational to our customer experience, operational excellence, and long-term differentiation. This role ensures we scale intelligently, innovate responsibly, and treat data as a strategic product—not merely a byproduct of systems. The Enterprise Architect – Data & AI is a senior, strategic role responsible for defining and governing the enterprise-wide data and AI architecture that enables scalable SaaS growth, trusted analytics, and responsible AI adoption. This architect serves as a bridge between business strategy, data platforms, analytics, and AI/ML capabilities, partnering closely with product, engineering, security, compliance, and business leaders to translate vision into durable competitive advantage. What you'll do Enterprise Data & AI Strategy - Define and maintain the enterprise data and AI architecture vision, principles, and roadmap aligned with business strategy and SaaS growth objectives - Establish data-as-a-product practices, including ownership models, quality standards, SLAs, discoverability, and lifecycle management - Guide strategic platform decisions across data ingestion, storage, processing, analytics, ML, and AI enablement AI Enablement & Responsible Governance - Partner with product and engineering teams to embed AI/ML capabilities into SaaS products and internal workflows - Define AI architecture patterns, such as feature stores, model lifecycle management, vector databases, and LLM integration - Work with Chief Data & AI Officer to establish responsible AI guardrails, including governance, security, privacy, explainability, and regulatory compliance - Collaborate with Chief Data & AI Officer, Security and Legal teams to align AI and data architectures with evolving regulatory requirements Architecture Governance & Standards - Create and enforce enterprise standards and reference architectures for data platforms, analytics, and AI services - Review and guide solution architectures to ensure scalability, interoperability, cost efficiency, and architectural consistency - Balance near-term innovation with long-term architectural sustainability Business & Technology Alignment - Translate business objectives into data and AI capabilities that drive measurable outcomes such as growth, efficiency, and customer experience - Advise executives on data and AI investment decisions, trade-offs, and risk management - Act as a trusted thought partner to senior leaders across product, engineering, and the business Platform & Cloud Architecture - Guide cloud-native data and AI architectures across modern SaaS stacks, including event-driven, API-first, and multi-tenant environments - Influence the evolution of data lakes, warehouses, streaming platforms, ML platforms, and AI services - Optimize architectures for scalability, reliability, cost management, and vendor portability Change Enablement & Thought Leadership - Evangelize data and AI best practices across the organization - Mentor architects and senior engineers on data and AI architecture patterns What you'll bring - 15+ years of experience with 10+ years of experience in enterprise, solution, or platform architecture, with deep focus on data and analytics - Proven experience designing enterprise-scale data platforms in cloud-based SaaS environments - Strong expertise in AI-enabled system architecture, including ML and/or Generative AI solution patterns - Solid understanding of AI/ML architectures, including data pipelines, model lifecycle management, and integration patterns - Hands-on familiarity with modern data technologies such as cloud data warehouses, data lakes, and streaming platforms - Experience working across product, engineering, security, and business stakeholders - Strong executive communication and storytelling skills - Pragmatic familiarity with enterprise architecture frameworks (e.g., TOGAF) and architecture governance - Proficiency in Agile and Lean delivery models - Ability to deliver work which meets all minimum standards of quality, security, and operability. Preferred Attributes - Experience enabling AI-powered SaaS products or large-scale analytics platforms - Experience with LLMs, generative AI, and vector-based architectures - Experience with data governance, privacy, and regulatory frameworks (e.g., SOC 2, GDPR, HIPAA, where applicable) - Experience with data mesh or domain-oriented data architectures - Background in a high-growth SaaS or platform company - Experience with observability for data and AI, including quality, drift, lineage, and cost/usage Stay up to date on everything Blackbaud , follow us on Linkedin, X, Instagram, Facebook and YouTube Blackbaud powers social impact through purpose‑driven technology and responsible AI. Guided by our Intelligence for Good® vision, we’re building a culture where innovation, trust, and human expertise come together to help organizations make a greater difference in the world. Originally posted on Himalayas

Наблюдалась 2026-10-07, впервые 2026-10-07, источник — Himalayas (JSON API).

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