Enterprise Data and AI Architect, Vice President

Mitsubishi UFJ Financial Group•Jersey City, NJ

6d•Hybrid

About The Position

We are building a high-impact architecture team focused on advancing enterprise data platforms, AI capabilities, and information architecture at scale. The Enterprise Data & AI Architect will lead the design and evolution of scalable, secure, and governed architectures that support analytics, operational workloads, and AI-driven innovation. This role spans data platforms, integration, semantic modeling, AI foundations, and enterprise governance, enabling consistent, reusable capabilities across the organization. This position supports multiple architect tracks, and candidates will be aligned based on their strengths and experience: Data Platform Architecture, Cloud Data & Integration Architecture, AI & Data Architecture, Knowledge / Semantic Architecture, Data Product & Information Architecture, Enterprise Data Architecture.

Requirements

8+ years of experience in: Data Architecture, Platform Architecture, AI Architecture or related fields.
Proven experience designing enterprise-scale data platforms and integration solutions.
Strong expertise in: Data storage, processing, and integration architectures.
Cloud platforms (AWS, Azure, or GCP).
Modern data paradigms (lakehouse, warehouses, APIs, event-driven systems).
Demonstrated ability to translate business requirements into scalable architectures.
Strong communication and stakeholder engagement skills.
Experience working in cross-functional enterprise environments.
Areas of Specialization (One or More): A. Data Platform & Integration (ETL / ELT, CDC, streaming, APIs, Scalability, performance optimization, resiliency), B. AI & Data Architecture (AI / ML, Generative AI, RAG, AI-ready data foundations and MLOps alignment), C. Knowledge & Semantic Architecture (Ontologies, knowledge graphs, semantic modeling, Metadata, lineage, and AI trust frameworks), D. Information & Data Product Architecture (Data products and domain-aligned data models, Metadata strategy and data governance), E. Enterprise Data Architecture (Architecture standards, operating models, and governance, ARB participation and capability frameworks).
Bachelor's degree in Computer Science or a closely-related discipline, or an equivalent combination of formal education and experience.

Nice To Haves

Experience with modern platforms such as: Snowflake, Databricks.
Exposure to: Data mesh or federated data models.
Experience in regulated environments (e.g., financial services).
Familiarity with: Metadata platforms, Governance tooling.
Knowledge of: Infrastructure-as-Code, CI/CD, MLOps practices.

Responsibilities

Data Platform & Integration Architecture: Design scalable enterprise data platforms (lakehouse, data warehouse, streaming, ingestion layers). Define modern integration patterns: ETL / ELT, Change Data Capture (CDC), APIs, and event-driven architectures. Drive platform modernization, interoperability, and data movement strategies.
AI & Knowledge Architecture: Architect AI-ready data foundations for analytics, machine learning, and generative AI. Define and implement: Semantic models, Ontologies, Metadata standards, Knowledge graph frameworks. Enable advanced AI use cases including: Retrieval-Augmented Generation (RAG), Semantic search, Explainable AI patterns.
Information & Data Product Architecture: Design business-aligned data products and reusable data assets. Define architectures for: Metadata management, Data lineage, Data quality and governance. Enable trusted, discoverable, and reusable data ecosystems.
Enterprise Architecture & Governance: Establish architecture standards, patterns, and reference models. Participate in Architecture Review Boards (ARB) and governance processes. Ensure alignment with enterprise strategy, risk, and compliance requirements.
Architecture Enablement & Collaboration: Partner across engineering, cloud, AI, and governance teams. Develop reusable: Architecture templates, Standards and guidelines, Onboarding frameworks. Provide hands-on guidance to delivery teams across platforms.