TEAM LEADERSHIP & CAPABILITY BUILDING

• Act as both technical authority and people mentor for Data Engineers
• Provide technical direction, architecture guidance, and engineering standards
• Conduct code reviews, technical coaching, and career development planning
• Identify skill gaps and establish learning pathways for cloud engineering, data architecture, automation, and modern data engineering practices
• Foster a culture of continuous learning, innovation, collaboration, and accountability
• Guide team engineers while DIRECTLY CONTRIBUTING to design, coding, and solving complex, ambiguous technical problems (not management-only role)

DATA PLATFORM MODERNIZATION & CLOUD MIGRATION

• Lead migration execution of enterprise data platforms from on-premises to cloud in alignment with approved enterprise roadmap
• Design scalable, secure, resilient, and cost-effective cloud data platforms
• Establish infrastructure automation, CI/CD pipelines, monitoring, and operational governance
• Build scalable data serving layers, semantic models, and data marts
• Ensure platform availability, scalability, disaster recovery, and operational excellence
• Ensure data is accessible, reliable, well-governed, and optimized for business consumption
• Implement data quality controls, reconciliation processes, and automated monitoring
• Improve end-to-end data freshness and delivery latency for reporting, analytics, and AI use cases

ENTERPRISE DATA ARCHITECTURE & GOVERNANCE IMPLEMENTATION

• Demonstrate deep practical understanding of Enterprise Data Architecture and translate architecture principles into implementable engineering patterns
• Apply enterprise data architecture principles across platforms and business domains
• Define and maintain data models, data standards, metadata structures, and integration patterns
• Embed strong data governance controls, lineage tracking, and metadata management into the data lifecycle
• Establish and enforce data ownership, stewardship, and data quality practices across business domains
• Ensure compliance with PDPA, internal security policies, audit requirements, and corporate governance standards
• Implement strict access controls, data masking, and encryption standards in line with enterprise and group security policies
• Drive adoption of modern architecture approaches (Data Lakehouse, Data Mesh, and event-driven patterns) where appropriate and beneficial

APPLICATION & ENTERPRISE INTEGRATION

• Build seamless data exchange pipelines between the core cloud data platform and transactional business applications
• Design and implement integrations across core systems, CRM platforms, customer applications, and external data providers
• Implement modern integration patterns including APIs, CDC, event streaming, and batch processing
• Ensure low-latency, reliable, secure, and scalable data movement across enterprise systems

Key Impacts:

• Ensure high-quality, AI-ready data ingestion at scale, reducing time-to-market for enterprise AI assets
• Establish a secure and compliant data foundation that protects enterprise data privacy during analytics and AI interactions
• Eliminate critical data bottlenecks in production environments and improve latency/reliability for enterprise RAG and search workloads
• Transition traditional warehouse/lake patterns toward future-ready graph- and vector-enabled AI data infrastructure where use cases justify adoption