APAC Head of Data Design & Models

The Japan Research Institute, Limited (Singapore Branch); Singapore; On-site; SGD 260,000 - 420,000; Full time

Job summary

SMBC Singapore seeks a Director/Executive Director, Data Design and Models to lead APAC-wide data design standards and scalable models that support reporting, analytics, and AI workloads.

You will define canonical data models, pursue data mesh-aligned architectures, and collaborate with Data Platform Engineering and AI teams to ensure scalable, compliant data assets across cloud and on-prem environments.

Qualifications

• 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.

Responsibilities

• Lead the design of APAC-wide data structures and integration frameworks, defining data organization and usage across domains.
• Define and maintain canonical data models and metadata standards for operational, analytical, and AI workloads.
• Establish standards for logical and physical modeling across the organization, including structured and unstructured data.

Skills

Data architecture

Enterprise data modeling

Leadership & influence

Stakeholder management

Regulatory data design

Tools

Databricks

Snowflake

Kafka

Spark

Delta Lake

Job description

SMBC Singapore seeks a Director/Executive Director, Data Design and Models to lead APAC-wide data design standards and scalable models that support reporting, analytics, and AI workloads.

You will define canonical data models, pursue data mesh-aligned architectures, and collaborate with Data Platform Engineering and AI teams to ensure scalable, compliant data assets across cloud and on-prem environments.