Director, Data Architecture (AI Enablement)

Digital Asset • New York City, NY

10h • Hybrid

About The Position

Digital Asset is hiring a Director, Data Architecture to own the enterprise-wide architecture and strategy for making organizational information discoverable, trustworthy, and securely available to AI assistants, agents, and search tools. The role spans structured and unstructured information, including databases, data warehouses, email, documents, wikis, knowledge bases, collaboration tools, and ticketing systems. It focuses on data enablement rather than model training or development.

The Director will initially work hands-on as a team of one, setting foundational architecture, standards, and platform direction, and will build and lead a team of data architects and engineers as the company scales. The role partners with Security, Legal, Privacy, product, engineering, business, and executive leaders. It includes enterprise architectural accountability, roadmap and budget ownership, vendor strategy, and responsibility for secure, permission-aware AI retrieval and data readiness.

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.
• Expertise with cloud and data platforms, including GCP and Snowflake, Databricks, BigQuery, or Redshift, and lakehouse formats such as Delta or Iceberg.
• Expertise with integration technologies and patterns, including CDC tools, REST and GraphQL APIs, webhooks, and OAuth.
• Experience with source systems including Google Workspace, Notion, Confluence, Jira, Slack, Salesforce, and Netsuite.
• Expertise in unstructured data processing, including document parsing and OCR, chunking, metadata extraction, entity recognition, and deduplication.
• Expertise in search and retrieval, including vector databases, hybrid keyword and semantic search, reranking, and embedding pipelines.
• Expertise in security, including IAM, ACL propagation, encryption, masking, tokenization, DLP, and row- and column-level security.
• Engineering expertise with SQL, Python, CI/CD, and Pulumi sufficient to guide and review team design decisions.
• Clear executive-level communication and strong stakeholder-management skills.
• Ability to build consensus across IT, security, legal, and business teams.
• Pragmatic prioritization across 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 (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.

Responsibilities

• Own the enterprise target-state data architecture for AI enablement across data stores, integration layers, and a unified access layer for 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 that expose enterprise data to AI tools.
• Own a 12-month delivery roadmap and multi-year data and AI-readiness strategy aligned to business priorities and budget, and secure executive sponsorship.
• Define ingestion approaches for structured sources using batch, change-detection, and streaming patterns.
• Define 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, including 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 partner with Security, Privacy, and Legal on applicable regulatory requirements.
• Define data-readiness criteria covering completeness, freshness, duplication, ownership, access rights, and content quality.
• Establish measures for retrieval quality, coverage, and stale content, 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 and ensure team outputs meet the standards of a scaling, high-growth environment.
• Build the team over time by 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 data requirements and investment priorities.
• Own vendor strategy, build-versus-buy analyses, and related budget.
• Report progress, trade-offs, and risks to senior and executive stakeholders, escalating early with proposed mitigations.

Benefits

• Comprehensive benefits package supporting team members’ well-being.
• Health and insurance benefits that vary by region and may include medical, dental, and vision coverage.
• Family-forward benefits and flexible working models.

What This Job Offers

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