Ngân hàng Oricombank - OCB ★ 3.2

- Develop and maintain Enterprise Data Architecture, Data Architecture Principles, standards, and blueprints aligned with business strategy and technology direction.

- Analyze business capabilities, data requirements, and existing systems to design target-state data architectures and transition roadmaps.

- Design end-to-end data flows and data integration architectures across source systems, Data Warehouse, Data Lake / Lakehouse, Data Mart, BI, and downstream applications.

- Design conceptual, logical, and physical data models to ensure consistency, scalability, performance, reusability, and alignment with enterprise data standards.

- Define and govern data architecture patterns for data ingestion, integration, transformation, storage, serving, and consumption, including Batch, Near Real-time, and Streaming architectures.

- Design and review Data Warehouse, Data Lake / Lakehouse, Data Vault, and Data Mart architectures based on business and technical requirements.

- Define standards and guidelines for data modeling, naming conventions, data structures, metadata, data lineage, data quality, and data lifecycle management.

- Review and approve data architecture and data model designs proposed by project teams, vendors, and implementation partners.

- Collaborate with Data Governance, Data Engineering, BI, Application Architecture, Infrastructure, Security, and business teams to ensure data solutions are aligned with enterprise architecture and governance requirements.

- Identify architectural risks, technical debt, data duplication, and inconsistencies across systems, and propose appropriate remediation and modernization approaches.

- Evaluate new data technologies, platforms, and architectural patterns and provide recommendations based on business value, scalability, performance, security, and total cost of ownership.

- Support project teams during implementation to ensure delivered solutions comply with approved architecture and design.

- Build and maintain architecture artifacts, including Data Architecture Blueprints, Data Flow Diagrams, Conceptual and Logical Data Models, architecture principles, standards, decision records, and roadmaps.

- Perform other tasks as assigned by management.

- Bachelor’s degree in Computer Science, Data Science, Information Systems, Software Engineering, or equivalent practical experience.

- Strong background in Data Architecture, Data Engineering, Data Warehouse, or enterprise-scale data platforms.

- Strong understanding of Data Architecture principles, Enterprise Data Architecture, Data Integration Architecture, and modern data platform architectures.

- Strong knowledge of Data Warehouse, Data Lake, Lakehouse, Data Mart, Operational Data Store, and database design principles.

- Strong hands-on experience in data modeling, including Conceptual Data Model, Logical Data Model, and Physical Data Model.

- Solid understanding of dimensional modeling, normalized data models (3NF), and enterprise data modeling techniques.

- Hands-on experience with Databricks (Cloud-based) or Oracle Data Warehouse environments for designing enterprise data solutions (mandatory requirement).

- Experience in relational databases and data platforms, including Oracle, SQL Server, MySQL, and DB2 (DB2 is highly preferred).

- Strong understanding of data integration patterns, including Batch, CDC, API-based integration, Event-driven, Near Real-time, and Streaming.

- Experience in designing end-to-end data flows from source systems through ingestion, storage, transformation, and consumption layers.

- Experience with Cloud platforms (AWS / Azure / GCP) and cloud-based data architectures.

- Ability to translate business requirements and business capabilities into scalable data architecture and data models.

- Ability to review technical designs, identify architectural risks and trade-offs, and provide clear recommendations.

- Strong analytical thinking, structured problem-solving, and ability to work with complex enterprise environments.

- Strong communication and stakeholder management skills, with the ability to collaborate across business, architecture, engineering, infrastructure, and vendor teams.

- Experience with Agile Software Development and a solid understanding of Agile principles, Scrum methodology, and collaborative delivery models.

- Team player with a proactive attitude and willingness to continuously learn and self-develop.

Nice to Have (Strong Plus):

- Experience or knowledge of IBM Banking Data Model or other enterprise banking data models.

- Experience with Data Vault 2.0, including Raw Vault, Business Vault, PIT, and Bridge structures.

- Experience with Databricks Lakehouse Architecture, Unity Catalog, and Medallion Architecture.

- Understanding of banking data domains such as Customer, Account, Product, Transaction, Finance, Risk, and Regulatory Reporting.

- Experience with Data Governance concepts and tools, including Business Glossary, Metadata Management, Data Lineage, Data Quality, and Data Ownership.

- Understanding of DataOps practices, including CI/CD, automated testing, monitoring, logging, and data quality automation.

- Experience with Enterprise Architecture frameworks or methodologies such as TOGAF.

- Experience working with large-scale data transformation or legacy Data Warehouse modernization programs.