Headquartered in Tokyo, Sumitomo Mitsui Banking Corporation (SMBC) is a leading global financial institution and a core member of Sumitomo Mitsui Financial Group (SMBC Group). Built upon our rich Japanese heritage since 1876, we put our customers first and provide seamless access to, from and within the Asia Pacific region. SMBC is one of the largest Japanese banks by assets and maintain strong credit ratings across our global integrated network. We work closely as one SMBC Group to offer personal, corporate and investment banking services to meet the needs of our customers.

With sustainability embedded within our strategy and operations, we are committed to creating a society in which today's generation can enjoy economic prosperity and well-being, and pass it on to future generations.

Key Responsibilities

• Lead the design of APAC-wide and contribute to global standard for data structures and integration frameworks, defining how data is organized, connected, stored, and consumed across business domains.
• Define and maintain canonical data models, domain data models, enterprise information structures, metadata standards, and manifest specifications to support operational, analytical, regulatory, and AI workloads.
• Establish standards and best practices for logical, and physical data modeling across the organization, including structured and unstructured data modeling approaches.
• Design semantic models and business data layers that provide consistent and reusable definitions for reporting, analytics, and AI use cases.
• Define enterprise data product design standards, ensuring data assets are scalable, discoverable, reusable, and aligned with data mesh principles.
• Develop reference designs and reusable patterns for data ingestion, integration, transformation, and distribution across cloud and on-premise environments.
• Establish standards for data ingestion, transformation frameworks, streaming data pipelines, event-driven platforms, metadata-driven workflows, and API-based data services.
• Define and create technical capabilities of data quality frameworks, validation controls, metadata management practices, and engineering guardrails that improve the reliability and integrity of enterprise data assets.
• Partner with Data Platform Engineering teams to ensure platform capabilities align with current and future data design, metadata, and AI enablement requirements.
• Collaborate with AI and Analytics teams to design AI-ready data structures, feature engineering standards, vectorized data models, knowledge representations, and agentic-ready platform design patterns.
• Review and approve strategic data design decisions, ensuring alignment with enterprise standards, scalability requirements, and long-term technology strategy.
• Drive continuous improvement of data design methodologies, metadata management practices, manifest-driven development standards, tooling, and engineering practices across the organization.
• Deep understanding of BCBS239 and various SMBC branch regulations and GDR requirements for regulatory reporting
• Create and maintain data platform detailed design, domain models for BCBS239, Product domains analytics

Qualifications & Skills

• Minimum 12 years' experience in enterprise data design, solution design, or large-scale data engineering leadership roles.
• Proven expertise designing enterprise data ecosystems and large-scale data platforms in complex financial services environments.
• Deep knowledge of conceptual, logical, physical, dimensional, domain-driven, and unstructured data modeling methodologies.
• Strong experience designing modern data platforms including Data Lakehouse, Data Mesh, Data Fabric, event-driven ecosystems, and API-based integration patterns.
• Hands-on knowledge of enterprise data platforms such as Databricks, Snowflake, Kafka, Spark, Delta Lake, and cloud-native data services.
• Experience defining standards for data transformation frameworks, streaming platforms, metadata-driven development, manifest design, and data product engineering.
• Strong understanding of semantic layers, metadata management, structured and unstructured data models, and analytical data modeling techniques.
• Experience supporting AI and machine learning platforms through scalable feature engineering, analytical data models, knowledge layers, and AI-ready data structures.
• Strong governance, technical leadership, and stakeholder management capabilities with the ability to influence engineering teams and senior stakeholders.
• Banking and financial services experience across regulatory, risk, finance, customer, and transaction data domains preferred.