Director/Executive Director, Data Design and Models

About the Role

We are seeking a senior leader to spearhead the architecture of data design frameworks across the APAC region, contributing also to global standards. This role involves leading the organization, structuring, and integration of data across multiple business sectors to support operational efficiency, analytical insights, regulatory compliance, and AI initiatives.

Key Responsibilities

• Lead the creation and governance of APAC-wide and global standards for data structures and integration frameworks, focusing on how data is organized, linked, stored, and utilized across business domains.
• Define canonical, domain-specific, and enterprise data models, along with metadata standards and manifest specifications to support diverse workloads including operational, analytical, regulatory, and AI functions.
• Establish best practices and standards for logical and physical data modeling, encompassing both structured and unstructured data formats.
• Design semantic and business data layers that offer consistent, reusable definitions to facilitate reporting, analytics, and AI applications.
• Develop and maintain enterprise data product design standards, ensuring assets are scalable, discoverable, reusable, and compliant with data mesh principles.
• Create reference architectures and reusable patterns for data ingestion, integration, transformation, and distribution across cloud and on-premises environments.
• Set guidelines for data ingestion, transformation frameworks, streaming pipelines, event-driven platforms, metadata-driven workflows, and API-based data services.
• Define and implement technical capabilities for data quality frameworks, validation controls, metadata management, and engineering guardrails to enhance data asset integrity and reliability.
• Collaborate closely with Data Platform Engineering to align platform capabilities with evolving data design, metadata management, and AI enablement requirements.
• Partner with AI and Analytics teams to develop AI-ready data structures, feature engineering standards, vectorized models, knowledge representation, and agentic platform design patterns.
• Review and endorse strategic data design decisions, ensuring adherence to enterprise standards, scalability needs, and long-term technology strategies.
• Continuously refine data design methodologies, metadata management practices, manifest-driven development standards, tooling, and engineering practices across the organization.
• Demonstrate thorough knowledge of BCBS239, SMBC branch regulations, and GDR reporting requirements, and develop detailed platform designs and domain models to support these.

Qualifications & Skills

• At least 12 years of experience in enterprise data design, solution architecture, or leadership roles in large-scale data engineering environments.
• Proven track record in designing enterprise data ecosystems and data platforms within complex financial services settings.
• Expertise in multiple data modeling approaches including conceptual, logical, physical, dimensional, domain-driven, and unstructured data models.
• Hands-on experience with modern data platforms such as Data Lakehouse, Data Mesh, Data Fabric, event-driven architectures, and API-based integrations.
• Practical knowledge of enterprise data technologies including Databricks, Snowflake, Kafka, Spark, Delta Lake, and other cloud-native data services.
• Experience setting standards for data transformation frameworks, streaming data platforms, metadata-driven development, and data product engineering.
• Strong grasp of semantic layers, metadata management, analytical data modeling, and handling of structured and unstructured data.
• Background supporting AI and machine learning platforms with scalable feature engineering, analytical data models, and AI-compatible data structures.
• Proven governance capability and technical leadership, with excellent stakeholder management skills to influence both engineering teams and senior leadership.
• Experience in banking and financial services, especially within regulatory, risk, finance, customer, and transaction data domains, is highly preferred.