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 related field (master's a plus)
Technical expertise
Cloud and data platforms: GCP; Snowflake, Databricks, BigQuery, or Redshift; lakehouse formats such as Delta or Iceberg
Integration: CDC tools, REST and GraphQL APIs, webhooks, OAuth
Source systems: Google Workspace, Notion, Confluence, Jira, Slack, Salesforce, Netsuite
Unstructured data: document parsing and OCR, chunking, metadata extraction, entity recognition, deduplication
Search and retrieval: Vector databases, hybrid keyword and semantic search, reranking, embedding pipelines
Security: IAM, ACL propagation, encryption, masking, tokenization, DLP, row and column-level security
Engineering: SQL and Python; CI/CD; Pulumi (sufficient depth to guide and review team design decisions)
Leadership qualities
Clear, executive-level communication and strong stakeholder management
The ability to build consensus across IT, security, legal, and business teams
Pragmatic prioritization between ideal architecture, risk, cost, and speed
A genuine commitment to developing people and leaders
The ability to unblock teams before tactical issues become strategic risks
Nice to Have
Experience with enterprise search, knowledge management, or content management platforms
Familiarity with generative AI patterns (RAG, agents, MCP) from the data-supply side
Knowledge of records management, eDiscovery, and legal hold
Certifications such as AWS, Azure, or GCP architect credentials
FinOps experience managing data platform and indexing costs
Experience in regulated industries such as financial services, healthcare, or insurance
What Success Looks Like
In your first year, you will have:
Connected, cataloged, and governed our priority structured and unstructured data sources
Delivered audited, permission-aware retrieval with zero access-leak incidents
Reduced the time to onboard a new data source for AI use
Established reference architectures and reusable data products adopted across teams
Raised content freshness and retrieval quality across our key knowledge sources
Presented the multi-year roadmap and AI data-risk posture to executive leadership
The range below may vary if based outside the New York tri-state area (New York, New Jersey, and Connecticut). Base salaries are determined during our interview process, during which we assess the candidate's experience, skills, and capabilities against internal peers and the scope and responsibilities of the position.
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