Senior Director, Data and AI Architecture Leader

Role Summary

Senior Director, Data and AI Architecture Leader. This position can be 100% remote, but must be located in the United States. This role leads strategic data architecture initiatives across the enterprise, designing and implementing a robust data ecosystem that supports business-wide processes with a focus on Data and AI enablement and digital transformation. They will play a key role in our cloud migration initiative, helping transform the enterprise data architecture into a modern, cloud-native ecosystem.

Responsibilities

• Central accountability to implement responsible Data and AI practices that comply with evolving regulations (e.g. GDPR; EU AI Act or Industry standards) and Dynavax ethical principles.
• Lead efforts to streamline our data processes, improve data quality and consistency, and enable more efficient analytical capabilities across the organization.
• Build an AI-ready data foundation that allows us to leverage machine learning and artificial intelligence to enhance business decision-making across all domains.
• As a member of the Dynavax Data and AI committee this position will play an important role in advising the Committee on AI solutions, investments and benefit analysis (e.g. ROI).
• Work collaboratively across the business and IT and demonstrate excellent communication and stakeholder management skills.
• Help improve data and AI Literacy across the organization.

Qualifications

• Required: Bachelor's degree in computer science, engineering, or related technical field.
• Preferred: Master's degree.
• Required: 8+ years of experience in enterprise data architecture or systems design.
• Required: Strong knowledge of AI operating model and data governance, lineage, and metadata management principles.
• Required: Ability/experience to establish data governance councils, stewardship, and quality frameworks.
• Required: Proven experience with large-scale data migration projects and cloud transformation.
• Required: Deep understanding of enterprise architecture frameworks and methodologies.
• Required: Familiarity with data operability standards (HL7/FHIR; CDISC; OMOP).
• Required: Strong technical background in database platforms (Databricks; Snowflake; Amazon Redshift).
• Preferred: Understanding of cloud architecture principles (AWS).
• Required: Enterprise systems architecture.
• Required: Data modeling and design.
• Required: Data lineage and governance tools.
• Required: ETL/ELT processes.
• Required: Big data and analytics platforms.
• Required: Financial risk systems and methodologies.
• Required: Programming knowledge (Python, SQL, etc.).
• Required: DevOps and CI/CD principles.
• Required: Knowledge graph and semantic web technologies.
• Required: Machine learning operations (MLOps).
• Required: AI-focused data catalog tools.
• Required: Schema registry and metadata management systems.
• Required: Feature store architecture for machine learning.
• Required: Ability to influence executives and translate complex technical concepts for non-technical stakeholders.