Senior Data Architect Senior Data Architect …

OCBC Bank
in Singapore
Permanent, Full time
Be the first to apply
Competitive
OCBC Bank
in Singapore
Permanent, Full time
Be the first to apply
Competitive
OCBC Bank
Senior Data Architect
At OCBC, we do not just merely provide a working space. We create a culture that allows you to flourish. Across the Bank, our leaders encourage fresh ideas, reward continuous learning and most importantly, we care for not just you but also your family. We recognize your drive, passion and talent. We will bring out the best in you and empower you to excel. Fulfil your life goals and career ambitions with us.
Role
The Senior Data Architect role is part of Enterprise Architecture team in OCBC Group Operations and Technology division, focusing on the transformation the existing data architecture and build of the next generation data platform across OCBC Group to support the bank's Digital Transformation.
You will bring a vision to modernize OCBC data architecture and develop a multi-year roadmap to bring that vision to life, collaborating with business and technology leads, application architects, data science and data engineering teams across the bank to enable large scale machine learning and analytics use cases.
You will act as a thought leader for the organization, defining data platform processes and best practices - engaging senior stakeholders across the group to ensure their continued buy-in on strategic data initiatives.
Responsibilities
- Define roadmap to transform data architecture focusing on scalability, performance and flexibility throughout the entire data life cycle (ingestion, storage and consumption).
- Maintain data architecture framework, standards and principles including modeling, metadata, security, master and reference data
- Define reference architecture as a set of patterns that can leveraged by diverse parts of the direction to create and improve data systems.
- Lead architectural designs solution context diagram and conceptual data model to optimize security, information leverage and reuse, integration, performance, and availability and ensure solutions developed adhere and aligns to the delivered architecture.
- Consult and influence digital application teams regarding solutions. Collaborate with other staff to design and implement effective technology solutions, while using innovative business and technology processes to identify and implement improvement initiatives, eliminate redundancies and maximize the reuse of data.
- Work closely with Solutioning, Infrastructure and project teams to understand their needs and ensure the best data architecture is implemented.
- Provide training and share best practices across teams regarding data architecture design and solution implementation including review and quality assurance.
- Develop and apply industry best practice technology, design and methodology approaches to design bank's data architecture. Research and recommend new emerging technologies, techniques and tools that will add value to the organization

Qualifications
Requirements
  • Bachelor's Degree in Computer Science or related discipline.
  • 10+ years of experience designing and building high performance resilient data architectures
  • Strong experience with traditional data technologies (ODS, Data Lake, Data Warehouse).
  • Experience in designing data patterns to support micro-service based application architecture
  • Track record of successfully building container-based big data architectures on top of Kubernetes.
  • Experience in designing systems to efficiently handle real-time and batch use-cases.
  • Exposure and understanding in the latest open source technologies across the big data ecosystem, such as distributed storage, real-time event processing and large scale distributed OLAP engines.
  • Exposure to Data as a Service concepts and data virtualization
  • Working knowledge of data science processes and best practices, with experience in building scalable architectures through the use of data science workflow orchestrators
  • Hands on experience on DevOps / DataOps / MLOps concepts
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