Enterprise Data Architect at East West Bank

Job description

Since 1973, East West Bank has served as a pathway to success. With over 110 locations across the U.S. and Asia, we are the premier financial bridge between the East and West. Our teams of experienced, multi-cultural professionals help guide businesses and community members on both sides of the Pacific looking to explore new markets and create new opportunities, and our sustained growth and expertise in industries like real estate, entertainment and media, private equity and venture capital, and high-tech help build sustainable businesses and expand our associates’ potential for career advancement. Headquartered in California, East West Bank (Nasdaq: EWBC) is a top-performing commercial bank with a strong foundation, an enterprising spirit and a commitment to absolute integrity. East West Bank gives people the confidence to reach further. East West Bank is seeking a highly experienced Enterprise Data Architect to lead the design, modernization, and governance of the bank’s enterprise data architecture strategy and AI-ready data ecosystem. This role is designed for a hands-on enterprise data architect with deep expertise in regulated financial services environments, modern cloud data platforms, enterprise data lifecycle management, and scalable AI/data-driven architectures. The ideal candidate combines strong technical depth with practical implementation experience and the ability to drive enterprise alignment across business, technology, data, risk, compliance, and governance stakeholders. The role will lead data architecture strategy and standards supporting enterprise analytics, AI/ML enablement, governance, regulatory reporting, operational intelligence, and modern data product delivery across the bank. • Define and lead the enterprise data architecture strategy, target-state roadmap, and modernization initiatives aligned to business priorities, AI enablement, and regulatory requirements. • Design and maintain scalable and secured enterprise data architectures enabling analytics, AI/ML, governance, regulatory reporting, and operational intelligence use cases. • Establish enterprise standards (approach, tooling, workflow) for data modeling, metadata management, lineage, reference architecture, semantic layers, integration patterns, and data lifecycle management. • Lead architecture design for Azure-native and cloud-based enterprise data platforms including Databricks, Azure Data Lake, Synapse, Microsoft Fabric, Azure Data Factory, and Power BI ecosystems. • Define reusable integration patterns across traditional ETLs, APIs, streaming platforms, operational systems, cloud platforms, and analytical environments. • Partner with data governance, cybersecurity, risk, compliance, and legal teams to ensure architecture controls support data quality, privacy, retention, lineage, auditability, and regulatory compliance. • Drive AI-ready architecture initiatives including lakehouse design, semantic modeling, vectorized data architectures, metadata-driven frameworks, and scalable AI data pipelines. • Partner with engineering, analytics, and business teams to operationalize scalable data products and enterprise data domains. • Evaluate and implement modern data architecture tooling including metadata/catalog platforms, observability solutions, orchestration frameworks, and governance technologies. • Lead enterprise data architecture reviews and governance processes to ensure alignment with approved standards, security controls, and target-state architecture principles. • Serve as a senior advisor to executive leadership, translating complex architecture concepts into actionable technology and business strategies. • Perform other duties as assigned. • 8+ years of progressive experience in enterprise data architecture, large-scale data platform modernization, data engineering, or solution architecture within financial services, fintech, insurance, or other highly regulated industries. • Strong hands-on experience designing and implementing enterprise data architectures in production environments, not solely strategy or governance roles. • Deep expertise in enterprise data lifecycle management including data modeling, integration architecture, metadata management, lineage, master/reference data management, retention, and governance. • Strong experience with Azure-native and modern cloud data ecosystems including Azure Databricks, ADLS Gen2, Synapse, Microsoft Fabric, Azure Data Factory, Spark, and distributed data platforms. • Strong understanding of AI/ML and advanced analytics architecture requirements including AI-ready data pipelines, semantic modeling, feature engineering support, and scalable analytical data environments. • Hands-on experience with enterprise data architecture tooling including Erwin, ER/Studio, Collibra, Microsoft Purview, Alation, Visio, Lucidchart, or equivalent platforms. • Strong understanding of modern architecture patterns including lakehouse architecture, event-driven systems, APIs, data mesh, data fabric, and distributed processing frameworks. • Practical experience implementing enterprise controls supporting data quality, security, privacy, access governance, auditability, and regulatory compliance. • Strong understanding of banking regulatory expectations including BCBS 239, SR 11-7, RCSA, CCAR, CECL, AML/BSA, and enterprise risk reporting principles. • Demonstrated ability to influence senior stakeholders and drive alignment across engineering, analytics, infrastructure, governance, cybersecurity, and business teams. • Excellent communication and executive presentation skills with the ability to simplify complex technical concepts for non-technical stakeholders. • Bachelor’s degree in Computer Science, Information Systems, Engineering, Data Architecture, or related technical discipline. Highly Preferred • Direct experience building enterprise AI/ML or GenAI-ready data platforms within banking or highly regulated industries. • Experience designing vectorized data architectures, semantic search platforms, feature stores, or AI-enabled data product ecosystems. • Hands on experience working with vector database tools such as Pinecone, Pgvector, Qdrant, etc. • Strong expertise in enterprise metadata, cataloging, governance, and lineage frameworks supporting regulatory transparency and audit readiness. • Experience leading enterprise-wide cloud data transformation or modernization initiatives at scale. • Hands-on experience with Kafka, event streaming platforms, API gateways, microservices, and real-time data processing architectures. • Architecture certifications such as Azure Solutions Architect Expert, Databricks Professional Architect, TOGAF, or equivalent. • Master’s degree in data science, Computer Science, Engineering, or related quantitative discipline. • Proven ability to operate as a highly autonomous enterprise architect balancing strategy, governance, and hands-on technical leadership. Applicants must have legal authorization to work in the United States. We do not offer visa sponsorship at this time. The base pay range for this position is USD $130,000.00/Yr. - USD $200,000.00/Yr. Exact offers will be determined based on job-related knowledge, skills, experience, and location.

Requirements

Must-haves

East West Bank is seeking a highly experienced Enterprise Data Architect to lead the design, modernization, and governance of the bank’s enterprise data architecture strategy and AI-ready data ecosystem.
The role will lead data architecture strategy and standards supporting enterprise analytics, AI/ML enablement, governance, regulatory reporting, operational intelligence, and modern data product delivery across the bank.
Define and lead the enterprise data architecture strategy, target-state roadmap, and modernization initiatives aligned to business priorities, AI enablement, and regulatory requirements.
Design and maintain scalable and secured enterprise data architectures enabling analytics, AI/ML, governance, regulatory reporting, and operational intelligence use cases.
Establish enterprise standards (approach, tooling, workflow) for data modeling, metadata management, lineage, reference architecture, semantic layers, integration patterns, and data lifecycle management.
Lead architecture design for Azure-native and cloud-based enterprise data platforms including Databricks, Azure Data Lake, Synapse, Microsoft Fabric, Azure Data Factory, and Power BI ecosystems.
Define reusable integration patterns across traditional ETLs, APIs, streaming platforms, operational systems, cloud platforms, and analytical environments.
Partner with data governance, cybersecurity, risk, compliance, and legal teams to ensure architecture controls support data quality, privacy, retention, lineage, auditability, and regulatory compliance.
Drive AI-ready architecture initiatives including lakehouse design, semantic modeling, vectorized data architectures, metadata-driven frameworks, and scalable AI data pipelines.
Partner with engineering, analytics, and business teams to operationalize scalable data products and enterprise data domains.
Evaluate and implement modern data architecture tooling including metadata/catalog platforms, observability solutions, orchestration frameworks, and governance technologies.
Lead enterprise data architecture reviews and governance processes to ensure alignment with approved standards, security controls, and target-state architecture principles.
Serve as a senior advisor to executive leadership, translating complex architecture concepts into actionable technology and business strategies.
Perform other duties as assigned.
Strong hands-on experience designing and implementing enterprise data architectures in production environments, not solely strategy or governance roles.
Deep expertise in enterprise data lifecycle management including data modeling, integration architecture, metadata management, lineage, master/reference data management, retention, and governance.
Strong experience with Azure-native and modern cloud data ecosystems including Azure Databricks, ADLS Gen2, Synapse, Microsoft Fabric, Azure Data Factory, Spark, and distributed data platforms.
Strong understanding of AI/ML and advanced analytics architecture requirements including AI-ready data pipelines, semantic modeling, feature engineering support, and scalable analytical data environments.
Hands-on experience with enterprise data architecture tooling including Erwin, ER/Studio, Collibra, Microsoft Purview, Alation, Visio, Lucidchart, or equivalent platforms.
Strong understanding of modern architecture patterns including lakehouse architecture, event-driven systems, APIs, data mesh, data fabric, and distributed processing frameworks.
Practical experience implementing enterprise controls supporting data quality, security, privacy, access governance, auditability, and regulatory compliance.
Strong understanding of banking regulatory expectations including BCBS 239, SR 11-7, RCSA, CCAR, CECL, AML/BSA, and enterprise risk reporting principles.
Demonstrated ability to influence senior stakeholders and drive alignment across engineering, analytics, infrastructure, governance, cybersecurity, and business teams.

Nice-to-haves

Highly Preferred

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