About Digital Asset & the Canton Network

Digital Asset builds the technologies that are reshaping the foundations of financial markets. Our mission is to create the world’s most trusted, privacy-enabled, and interoperable digital infrastructure for global finance.

At the heart of this mission is the Canton Network - the blockchain purpose-built for institutional finance. Uniquely combining privacy, compliance, and scalability, Canton enables real-time, secure settlement across multiple asset classes. Governed by the Canton Foundation with participation from leading financial institutions, it is the proven link between the promise of blockchain and the power of global finance.

Founded in 2014 and headquartered in New York City, with offices in London, Zurich, Budapest, Hong Kong, and Sydney— we are on a mission to transform finance— and that transformation is now playing out rapidly on the Canton Network.

If you’re interested in playing a crucial role at the intersection of traditional and crypto-capital markets and want to help bring the next trillion dollars of finance on-chain, join us.

About the role:

AI is only as useful as the data it can safely reach. As Director, Data Architecture, you will own how our organization’s information, from databases to data warehouses to email, documents, Notion, Confluence, and Slack, becomes discoverable, trustworthy, and securely available to AI assistants, agents, and search tools.

This is a senior technical leadership role with enterprise-wide architectural accountability.  You will own the direction, make the platform and standards decisions, and partner with Security, Legal, Privacy, product, engineering, and business leaders to ensure AI retrieves the right information for the right people and processes. This role focuses on data enablement and does not involve model training or development. You will initially be a team of one, hands-on while setting the foundation, and will build and lead a team of data architects and engineers as the company scales.

Responsibilities:

Set the architecture and strategy

• Own the enterprise target-state data architecture for AI enablement, spanning our data stores, integration layers, and a unified access layer across structured and unstructured content
• Set reference architectures for retrieval-based AI: ingestion, parsing, chunking, embedding, indexing, hybrid search, and retrieval APIs
• Establish company-wide standards for connectors, data contracts, APIs, and MCP-style integrations that expose enterprise data to AI tools
• Own a 12-month delivery roadmap and a multi-year data and AI-readiness strategy aligned to business priorities and budget, and secure executive sponsorship for both