Director, Data Architecture (AI Enablement)

Digital Asset • New York, NY

10h • $225,000 - $260,000

About The Position

Digital Asset is seeking a Director, Data Architecture to own the enterprise data architecture that makes organizational information discoverable, trustworthy, and securely available to AI assistants, agents, and search tools. The role spans databases, data warehouses, email, documents, and collaboration and knowledge platforms. It focuses on data enablement rather than model training or development.

This is a senior technical leadership position with enterprise-wide architectural accountability. The Director will set platform direction and standards, partner with Security, Legal, Privacy, product, engineering, and business leaders, and ensure AI retrieval provides the right information to the right people and processes. The role begins as a hands-on individual contributor and is expected to build and lead a team of data architects and engineers as the company scales.

The Director will own the target-state architecture, reference designs, delivery roadmap, and longer-term data and AI-readiness strategy. The position also leads integration of structured and unstructured data, establishes semantic and metadata practices, governs permission-aware retrieval and sensitive content, and improves data readiness and retrieval quality. The Director will manage vendors and budgets, translate business and application use cases into data requirements, and communicate progress, trade-offs, and risks to senior and executive stakeholders.

Digital Asset builds digital infrastructure for global finance and the Canton Network, a privacy-enabled blockchain for institutional finance. The company was founded in 2014 and is headquartered in New York City, with offices in London, Zurich, Budapest, Hong Kong, and Sydney. The listed salary range is $225,000–$260,000 USD; the range may vary for candidates outside the New York tri-state area. Digital Asset offers comprehensive, family-forward benefits and flexible working models, with health and insurance benefits varying by region.

Requirements

• 10+ years in data engineering, data architecture, or information management.
• 5+ years leading architecture functions or teams, with experience hiring, mentoring, and influencing across organizations.
• Track record of delivering enterprise-scale cloud data platforms integrating many source systems.
• Experience leading enterprise programs integrating unstructured content 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.
• Technical knowledge of cloud and data platforms, including GCP and Snowflake, Databricks, BigQuery, or Redshift; lakehouse formats such as Delta or Iceberg.
• Technical knowledge of integration technologies including CDC tools, REST and GraphQL APIs, webhooks, and OAuth.
• Familiarity with source systems including Google Workspace, Notion, Confluence, Jira, Slack, Salesforce, and Netsuite.
• Knowledge of unstructured-data techniques including document parsing and OCR, chunking, metadata extraction, entity recognition, and deduplication.
• Knowledge of search and retrieval technologies including vector databases, hybrid keyword and semantic search, reranking, and embedding pipelines.
• Knowledge of security technologies and practices including IAM, ACL propagation, encryption, masking, tokenization, DLP, and row- and column-level security.
• Engineering skills including SQL and Python, CI/CD, and sufficient Pulumi depth to guide and review team design decisions.
• Clear executive-level communication and strong stakeholder management.
• Ability to build consensus across IT, security, legal, and business teams.
• Pragmatic prioritization among architecture, risk, cost, and speed.
• Commitment to developing people and leaders.
• Ability to unblock teams before tactical issues become strategic risks.

Nice To Haves

• Master’s degree.
• Experience with enterprise search, knowledge management, or content management platforms.
• Familiarity with generative AI patterns such as RAG, agents, and MCP from the data-supply side.
• Knowledge of records management, eDiscovery, and legal hold.
• AWS, Azure, or GCP architect certifications.
• FinOps experience managing data platform and indexing costs.
• Experience in regulated industries such as financial services, healthcare, or insurance.

Responsibilities

• Own the enterprise target-state data architecture for AI enablement across data stores, integration layers, and unified access to structured and unstructured content.
• Set reference architectures for retrieval-based AI, including ingestion, parsing, chunking, embedding, indexing, hybrid search, and retrieval APIs.
• Establish company-wide standards for connectors, data contracts, APIs, and MCP-style integrations exposing enterprise data to AI tools.
• Own a 12-month delivery roadmap and multi-year data and AI-readiness strategy aligned with business priorities and budget, and secure executive sponsorship.
• Set ingestion approaches for structured sources using batch, change detection, and streaming patterns.
• Set ingestion approaches for unstructured and semi-structured sources, including email, documents, wikis, collaboration tools, and ticketing systems.
• 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, using taxonomies, ontologies, and knowledge graphs where useful.
• Be accountable for permission-aware AI retrieval that respects source-system access controls and user entitlements.
• Lead data governance with Security, Privacy, and Legal, including cataloging, lineage, metadata, ownership, retention, and quality standards.
• Define enterprise policies for sensitive content in AI contexts, including PII, confidential material, redaction, consent, and legal hold.
• Shape the organization’s AI data-risk posture and work with Security, Privacy, and Legal on applicable regulatory requirements.
• Define data-readiness criteria covering completeness, freshness, duplication, ownership, access rights, and content quality.
• Establish measurement of retrieval quality, coverage, and stale content, and ensure evaluation sets are maintained.
• Prioritize cleanup of high-value and high-risk sources such as outdated wikis, conflicting policy documents, and orphaned files.
• Act as the final check on delivery quality and ensure the team’s output meets standards appropriate to a scaling, high-growth environment.
• Build the team over time by hiring and developing data architects and engineers, and establish the operating model for data practices across the organization.
• Translate AI use cases from application and business teams into clear enterprise 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.

Benefits

• Comprehensive, family-forward benefits package.
• Flexible working models.
• Health and insurance benefits that vary by region and may include medical, dental, and vision coverage.

What This Job Offers

• Job Type: Full-time
• Career Level: Director