Enterprise Data and AI Architect, Vice President

MUFG•Jersey City, NJ

7d•$172,000 - $191,000•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
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

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
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
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
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
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