Senior Data Engineer/ Data Architect (Retail Banking Digital Transformation)
Hạn nộp hồ sơ: 31/08/2026 (Còn 4 ngày)
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Mô tả công việc
Mô tả Công việc
- 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.
- 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.
Yêu cầu
Yêu Cầu Công Việc
- 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.
- 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.
Quyền lợi
Laptop
Chế độ bảo hiểm
Du Lịch
Phụ cấp
Đồng phục
Chế độ thưởng
Chăm sóc sức khỏe
Đào tạo
Tăng lương
Công tác phí
Phụ cấp thâm niên
Nghỉ phép năm
CLB thể thao
Chế độ bảo hiểm
Du Lịch
Phụ cấp
Đồng phục
Chế độ thưởng
Chăm sóc sức khỏe
Đào tạo
Tăng lương
Công tác phí
Phụ cấp thâm niên
Nghỉ phép năm
CLB thể thao
Thông tin chung
- Thu nhập: Cạnh tranh
Việc làm tương tự khác
Văn phòng đại diện công ty TNHH tập đoàn GFT
Hà Nội, Hồ Chí Minh
Thỏa Thuận
NGÂN HÀNG TMCP PHƯƠNG ĐÔNG (OCB)
Hà Nội, Hồ Chí Minh
Cạnh tranh
Công ty Cổ phần SmartOSC
Hà Nội, Hồ Chí Minh, Đà Nẵng
30 - 56 triệu
NGÂN HÀNG TMCP PHƯƠNG ĐÔNG (OCB)
Xem trang công ty- Địa chỉ công ty: 28-30 Huỳnh Thúc Kháng, Phường Bến Nghé, Quận 1, Tp. Hồ Chí Minh
- Quy mô: Từ 5000 - 10000 nhân viên
- Lĩnh vực: Ngân hàng/ Tài Chính
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