Director/Executive Director, Data Platform & Tools Engineering

As the Director/Executive Director of Data Platform & Tools Engineering for SMBC Asia Pacific, you will lead the engineering implementation, and evolution of the region's enterprise data platform and tooling ecosystem. You will be responsible for delivering scalable, secure, and resilient platform capabilities that enable data ingestion, transformation, analytics, AI, and regulatory reporting across the bank.

You will define and execute the technology roadmap for enterprise data platforms, frameworks, tooling, and infrastructure, ensuring they provide a modern, self-service, and cloud-native foundation for data products and analytical workloads. Working closely with Data Design & Models, Data Engineering & Delivery, Analytics, and AI teams, you will establish reusable platform capabilities, engineering standards, and operational practices that accelerate delivery while maintaining security, reliability, and cost efficiency.

Key Responsibilities:

. Lead the engineering, implementation, and continuous evolution of the enterprise data platform, ensuring scalability, resilience, performance, and operational excellence across all data workloads.

. Develop and operate cloud-native platform infrastructure supporting structured, semi-structured, and unstructured data processing across multiple jurisdictions.

. Develop and maintain enterprise data ingestion frameworks supporting batch, real-time, streaming, event-driven, API-based, and file-based integration patterns.

. Establish reusable ingestion accelerators, connectors, orchestration capabilities, and metadata-driven onboarding frameworks that improve consistency and reduce implementation effort.

. Lead the engineering and evolution of enterprise transformation frameworks, providing scalable data processing capabilities, reusable transformation patterns, code standardization, testing frameworks, and deployment automation.

. Own and manage enterprise business intelligence and visualization platforms, ensuring secure, scalable, and governed access to reporting and analytical capabilities.

. Establish and operate analytics workbench capabilities for analysts, data scientists, and AI engineers, including notebook environments, collaborative development platforms, feature engineering environments, and experimentation tooling.

. Lead the engineering of enterprise LLM integration capabilities, including model connectivity services, prompt orchestration frameworks, retrieval services, vector database integrations, and agent enablement capabilities.

. Develop reusable AI engineering services that enable secure consumption of internal and external foundation models while complying with enterprise security and regulatory requirements.

. Establish engineering standards and operating practices for Infrastructure-as-Code, environment provisioning, platform automation, release management, DevSecOps, and continuous delivery.

. Implement platform observability capabilities including monitoring, logging, telemetry, performance analytics, operational dashboards, and automated alerting.

. Lead platform capacity planning, workload optimization, resource utilization management, and performance tuning across data and AI environments.

. Own platform cost management and efficiency initiatives, including cloud financial management, workload optimization, storage lifecycle management, compute utilization monitoring, and vendor licensing optimization.

. Implement enterprise data security controls including encryption, tokenization, masking, secrets management, key management, identity integration, role-based access control, attribute-based access control, and data protection frameworks.

. Partner with Cyber Security, Infrastructure, and Risk teams to ensure compliance with enterprise security standards, regulatory obligations, and operational resilience requirements.

. Collaborate with Data Design & Models to ensure platform capabilities effectively support enterprise metadata standards, semantic frameworks, data products, and emerging business requirements.

. Collaborate with Data Engineering & Delivery teams to provide reusable engineering services, frameworks, tooling, and platform capabilities that accelerate delivery of business solutions.

. Manage strategic vendor relationships and technology investments across the enterprise data and analytics technology ecosystem, ensuring platform investments maximize business value and operational efficiency.

. Build and lead high-performing teams of platform engineers, cloud engineers, DevOps engineers, platform specialists, and engineering leads across Asia Pacific and the Global Capability Center.

. Continuously evaluate emerging technologies, engineering practices, and platform capabilities to improve developer productivity, operational efficiency, platform reliability, and business value realization.

Qualifications & Skills:

. Minimum 10 years' experience leading enterprise data platform engineering, cloud platform engineering, or large-scale technology organizations within complex financial services environments.

. Proven experience building and operating enterprise-scale data platforms supporting analytics, regulatory reporting, and advanced data workloads.

. Deep expertise in modern data platform technologies including Databricks, Snowflake, Kafka, Spark, Delta Lake, Airflow, Kubernetes, OpenShift, and cloud-native data services.

. Strong experience designing and implementing enterprise ingestion frameworks, transformation frameworks, event-driven architectures, streaming platforms, API integration frameworks, and distributed processing environments.

. Experience engineering enterprise analytics workbenches and data science platforms supporting advanced analytics, machine learning, and AI development.

. Strong knowledge of modern AI integration patterns including LLM integration frameworks, retrieval architectures, vector databases, model access services, and AI engineering practices.

. Experience implementing Infrastructure-as-Code, CI/CD pipelines, DevSecOps practices, platform observability, and automated operational controls.

. Strong expertise in cloud financial management, platform cost optimization, workload management, and large-scale platform operations.

. Deep understanding of enterprise security architecture including IAM, RBAC, ABAC, encryption, tokenization, secrets management, and data protection technologies.

. Proven experience leading geographically distributed engineering teams, platform transformation initiatives, and strategic technology vendor partnerships.

. Experience managing sizable technology budgets and optimizing both delivery and operational costs across multiple jurisdictions.

. Strong stakeholder management, communication, and influencing skills with the ability to drive enterprise-wide platform adoption and engineering excellence.

. Banking and financial services experience supporting multi-jurisdiction regulatory, risk, finance, and operational environments preferred