Data & AI Architect
Summary
Lead OCBC’s enterprise data and AI architecture strategy, defining blueprints for data collection, processing, and monetisation while ensuring compliance and driving business outcomes across the bank.
Who We Are As Singapore's longest established bank, we have been dedicated to enabling individuals and businesses to achieve their aspirations since 1932. How By taking the time to truly understand people. From there, we provide support, services, solutions, and career paths that meet their individual needs and desires. Today, we're on a journey of transformation. Leveraging technology and creativity to become a future-ready learning organisation. But for all that change, our strategic ambition is consistently clear and bold, which is to be Asia's leading financial services partner for a sustainable future. We invite you to build the bank of the future. Innovate the way we deliver financial services. Work in friendly, supportive teams. Build lasting value in your community. Help people grow their assets, business, and investments. Take your learning as far as you can. Or simply enjoy a vibrant, future-ready career. Your Opportunity Starts Here. Role Purpose Lead the enterprise data and artificial intelligence (AI) architecture strategy and blueprints that govern how data is collected, secured, processed, activated, and monetised across the bank. Act as design authority across programmes to ensure solution designs conform to target state and deliver business outcomes aligned to the enterprise architecture roadmap. Key Accountabilities
- Enterprise Blueprints & Roadmaps: Define and maintain the target-state Data & AI architecture (e.g., data mesh/lakehouse, streaming, analytics, MLOps, metadata/lineage) and domain roadmaps aligned to business strategy and regulatory obligations.
- CrossDomain Design Authority: Chair/participate in design reviews for initiatives on Data & AI domain which span across channels, core banking, risk, finance, and compliance; adjudicate architecture decisions and manage dispensations with clear remediation timelines.
- Standards, Patterns & Reference Architectures: Publish and evolve standards for data modelling, eventing, ML lifecycle, feature stores, model risk controls, privacy-by-design, and responsible AI; drive adoption through architecture governance gates.
- Solution Guidance & Quality Assurance: Partner with solution architects and engineering leads to shape designs (batch/stream, APIs, real-time scoring, vector stores/RAG, feature pipelines) and assure conformance to blueprint prebuild, during build, and pregolive.
- Risk, Controls & Compliance: Embed data quality, lineage, retention, encryption, and access controls. Ensure designs meet data privacy, model risk, and operational resilience requirements across jurisdictions.
- Value Realisation & Metrics: Define measurable outcomes (e.g., time-to-model, reuse of data products/patterns, cost-to-serve) and track via OKRs; inform investment prioritisation through architecture insights.
- People & Capability: Coach solution architects and engineers; curate reusable reference implementations and run patterns guilds to uplift Data & AI architecture competency across the organisation.
- 10–15+ years in enterprise/solution architecture with at least 6 years leading data/AI architectures at scale (streaming, batch, lakehouse, analytics, ML/GenAI).
- Proven ownership of enterprise data platform modernisation and MLOps across multicloud or hybrid environments.
- Strong grasp of data governance, lineage/metadata, model risk controls, and responsible AI practices, including privacy and security-by-design.
- Hands-on expertise with major cloud data/AI platforms (e.g., Azure Synapse/Fabric/ML, AWS Redshift/SageMaker, GCP BigQuery/Vertex AI), streaming (Kafka), orchestration, catalog/lineage tools.
- Excellent stakeholder management and communication across business, technology, risk, and compliance; able to influence senior leaders and arbitrate design decisions.
- Demonstrated ability to define new architectures, lead cross-domain programs, and drive adoption of standards and patterns with measurable outcomes.
- Architecture: TOGAF or equivalent.
- Cloud/Data/AI: Azure Solutions Architect Expert; Azure Data Engineer or AI Engineer (or AWS/GCP equivalents); Databricks Professional; SnowPro; DAMA CDMP.
- Security/Privacy: CISSP/CCSP or equivalent; data privacy certifications are advantageous.