Director/Executive Director, Data Design and Models
- Posted 25 days ago
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Job Description
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
- 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.
More Info
Key Skills
event-driven ecosystems
semantic layers
API-based integration patterns
data product engineering
enterprise data design
data transformation frameworks
metadata-driven development
analytical data modeling techniques
Data Lakehouse
AI-ready data structures
Delta Lake
feature engineering
Data Fabric
Data Mesh
data modeling methodologies
manifest design
large-scale data engineering
About Company
Director/Executive Director, Data Design and Modelssumitomo mitsui banking corporation singapore branch
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Skills:
snowflake , Metadata Management, Kafka, Spark, Databricks, API-based integration patterns, data product engineering, enterprise data design, metadata-driven development, AI-ready data structures, cloud-native data services, Data Mesh, data modeling methodologies, large-scale data engineering, event-driven ecosystems, semantic layers, data transformation frameworks, analytical data modeling techniques, Data Lakehouse, Delta Lake, feature engineering, Data Fabric, manifest design, streaming platforms
Skills:
snowflake , Ml, Metadata Management, Distributed Computing, Apis, Databricks, Data Governance, Data Analytics, Ai, Data Cataloging, Cloud-native Frameworks
Skills:
Data Analytics, AML Enterprise Wide Risk Assessment, quality assurance reviews, AML Control Self Assessment, Stakeholder Management, AML risk management, transaction reviews, thematic testing, process and control design