Director, Data Architecture (AI Enablement)

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

Connect structured and unstructured data

• Set the ingestion approach for structured sources (ERP, CRM, finance, HR, operational systems) using batch, change detection, and streaming patterns
• Set the ingestion approach for unstructured and semi-structured sources, including email, documents (PDF, documentation, spreadsheet, slides), wikis and knowledge bases (Notion, Confluence), collaboration tools (Slack, Google Drive), and ticketing systems (JIRA, Github)
• Define how content is parsed, cleaned, deduplicated, enriched with metadata, and kept fresh through incremental sync, change detection, and deletion handling
• Create and oversee a semantic and metadata layer (taxonomies, ontologies, knowledge graphs where useful) so content from different systems can be connected and searched coherently

Protect what matters

• Be accountable for permission-aware AI retrieval that respects source-system access controls and user entitlements
• Lead data governance in partnership with Security, Privacy and Legal , including cataloging, lineage, metadata, ownership, retention, and quality standards
• Define enterprise policies for sensitive content in AI contexts, such as PII, confidential material, redaction, consent, and legal hold
• Shape the organization’s AI data-risk posture and partner with Security, Privacy, and Legal to meet regulatory requirements (GDPR, CCPA, SOX, EU AI Act as applicable)

Raise data readiness and quality

• Define readiness criteria covering completeness, freshness, duplication, ownership, access rights, and content quality
• Establish how retrieval quality, coverage, and stale content are measured, and ensure evaluation sets are maintained
• Prioritize cleanup of high-value, high-risk sources such as outdated wikis, conflicting policy documents, and orphaned files
• Act as the final check on delivery quality, ensuring the team’s output meets the standards of a scaling, high-growth environment

Lead the team and the conversation

• Build the team over time, hiring and developing data architects and engineers, and set the operating model for data practices across the organization.
• Translate AI use cases from application and business teams into enterprise clear data requirements and investment priorities
• Own vendor strategy, build-versus-buy analyses, and the related budget
• Report progress, trade-offs, and risks to senior and executive stakeholders, escalating early with proposed mitigations

What We're Looking For:

Experience

• 10+ years in data engineering, data architecture, or information management
• 5+ years leading architecture functions or teams, with a track record of hiring, mentoring, and influencing across organizations.
• A track record of delivering enterprise-scale cloud data platforms that integrate many source systems
• Experience leading enterprise programs that integrate unstructured content (documents, email, wikis, collaboration tools) with structured data and search or retrieval architectures
• Experience designing or governing access control and entitlement models for enterprise data
• Proven delivery of data governance, cataloging, and data quality programs
• Experience managing budgets and vendor relationships
• Bachelor's degree in computer science, engineering, information systems, or a