Principal Architect, Data Knowledge Platform

Principal Architect, Data Knowledge Platform

Hiring Location: San Francisco

Team Introductions:

The Knowledge Platform team is at the heart of our company's data and AI strategy. We are building the foundational infrastructure that empowers the entire company to leverage data, AI, and ML into business impact. We accomplish this by creating platforms that handle the entire data and AI/ML lifecycle, simplifying the interface while providing proper safety and governance. This includes managing our data infrastructure (Databricks, Spark, Kafka, etc.), the technology to serve that data to our users (RAG, MCP, etc.), and the platform to host and govern these AI/ML models.

In 2026, our team's overarching mission is to evolve our full data ecosystem—encompassing both platform and models—into a fully AI agent-ready infrastructure; we will empower customers to engage directly with the data platform to extract actionable value through capabilities like analytics and natural language querying, while also upgrading the platform to deliver robust, real-time performance for instant, data-driven decision-making.

Who You Are? Role Overview:

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.

Responsibilities:

• 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.

Minimum Requirements:

• 10+ years of experience designing and building large-scale data systems with strong architectural fundamentals.
• Hands-on experience with the Databricks ecosystem, including Unity Catalog and Delta Lake.
• Experience implementing semantic layers or metric stores to enable consistent reporting and analytics.
• Solid understanding of how AI/agent-based systems consume data, with experience building APIs or data services to support them.
• Experience designing data solutions that meet regional data residency and compliance requirements.
• Demonstrated senior-level individual contributor experience, including leading design reviews and mentoring engineers.