We are looking for a Principal Architect who operates at the intersection of platform architecture, customer-facing data products, and AI enablement. This is not a traditional infrastructure role. Your mandate is to architect the governed, reusable data, knowledge, and skill layer that powers analytics, AI agents, and real-time decision systems across the business
As the most senior individual contributor on the team, you will be the primary technical authority responsible for evolving our platform beyond raw data storage into a structured system of three layers
Data Layer: You will design the patterns for governed, well-modeled structured datasets—from transactional records and event streams to dimensional models and feature tables. You will ensure high quality, strong lineage, and regional compliance are "baked in" to the foundation, keeping it clean, trusted, and queryable
Knowledge Layer: You will architect the semantic layer that makes data meaningful to both humans and machines. This includes business metric definitions, entity relationships, and contextual documentation. Your goal is to ensure an analyst or an AI agent can ask "what is our net revenue retention in SEA?" and receive a consistent, trustworthy answer without ever having to reverse-engineer a schema
Skills Layer: You will define the architecture for operationalized capabilities that act on data and knowledge. This includes reusable analytical workflows, agent-callable tools, automated pipelines, and transformation primitives. These are the building blocks that allow AI agents and internal teams to reason and act reliably, rather than just querying data
This role is based in Singapore
Own the technical execution of regional and global data localisation strategies within the Databricks environment
Design scalable patterns that satisfy strict regulatory requirements while maintaining a unified global data model across priority markets
Reconcile local compliance constraints with global consistency in definitions, metrics, and knowledge artifacts
Partner with Data Science, AI Engineering, Product, and Risk teams to ensure the platform supports experimentation and high-quality decision-making
Lead global data modeling efforts, creating unified schemas that accommodate regional variance
Define and implement “Agent Interfaces”—APIs and tools that enable AI agents to interact with Knowledge and Skills layers
Implement Governance-as-Code, ensuring automated data lineage and quality controls are embedded directly into the data platform
Who You Are?
We're looking for people who meet the minimum qualifications for this role. The preferred qualifications are great to have, but are not mandatory