Principal Data Architect

Delta Dental•Oakland, CA

About The Position

The Principal Data Architect is a leadership position responsible for developing the vision for AI-enabled data ecosystems, defining the Enterprise Data Strategy, and establishing the Data Architecture needed to realize business strategy and outcomes. This role ensures alignment of data across operational and analytical systems, enabling consistency from transaction capture through intelligent decision-making. Responsibilities include defining the vision, target state, roadmap, and governance associated with the management and use of data assets across the enterprise. In addition, this role serves as a thought leader and is responsible for enabling advanced analytics and AI/ML technologies, as appropriate, across the company.

Requirements

A minimum of 12 years of professional experience in IT, including at least 5 years specializing in information and data architecture.
Significant experience serving as a Data/Information Architect within a complex enterprise environment, with demonstrated success delivering scalable, enterprise-grade data solutions.
Hands-on experience with modern data and AI platforms such as Snowflake, Databricks, Azure, GCP, and/or AWS.
Deep understanding of data technologies and the supporting infrastructure, including cloud and cybersecurity technologies, required to deliver enterprise data capabilities.
Proven experience using data science and advanced analytics tools and technologies to solve complex enterprise problems.
Extensive experience designing and implementing information and data solutions.
Strong experience with AI/ML ecosystems, including model lifecycle management and supporting data requirements.
Experience with data science concepts, as well as MDM, business intelligence, and data warehouse design and implementation.
Experience with distributed data management and analytics in cloud and hybrid environments, including an understanding of multiple data access and analytics approaches (e.g., microservices and event ‑driven architectures).
System integration experience, including interface design and familiarity with web-oriented architecture patterns.
Expertise in enterprise-level data modeling and information classification.
Understanding of common information architecture frameworks and enterprise information models.
Knowledge of problem analysis, structured analysis and design, and programming techniques.
Strong business acumen with the ability to communicate, influence, and collaborate effectively with executives, business stakeholders, and technical teams.
Demonstrated leadership, influence, presentation, facilitation, and team ‑oriented problem ‑solving skills across multiple levels of the organization.
Ability to translate information architecture concepts and contributions into clear, business-focused outcomes and briefings for diverse data and analytics stakeholders.
Organizationally savvy, with the ability to navigate, influence, and persuade within complex enterprise environments.
Ability to think strategically and tactically, balancing long-term vision with near-term execution.
Ability to assess rapidly evolving technologies and apply them effectively to business needs.
Track record of remaining unbiased toward specific technologies or vendors.

Nice To Haves

Experience in the healthcare and/or insurance domain, particularly within payer organizations (e.g., insurance carriers), is highly preferred.
Hands-on experience implementing data and analytics governance or management programs is preferred.
3 to 5 years of experience as a data analyst is highly desirable, particularly experience focused on front-end data consumption, analytics, and business-facing insights.

Responsibilities

Own developing, documenting the Data Strategy: Own the development and documentation of Enterprise Data Strategy by collaborating with Data Architecture, Data COE and other functional data leaders. The Data strategy verbalizes the data capabilities that we intend to create within our enterprise and the roadmap to realize it. As data capabilities span multiple areas of concern including data governance, data security, AI/ML use of data etc. the individual must have supreme communication, collaboration with ability to influence alignment, agility to achieve outcomes.
Own Enterprise Data Architecture: By partnering with Data architecture and Enterprise architecture team and other business leaders, develop, evangelize, and govern the evolution of Data architecture within Enterprise. Data architecture includes documentation of current and target state of how data will be managed and consumed for transactional, operational, analytics use along with the right fit data technologies and their implementation patterns.
Enable Advanced analytics and AI/ML use cases: Provide thought leadership and specific technology expertise as it relates to successful application of advanced analytics and data science techniques within the enterprise.
Establish a vision for AI-enabled Data ecosystems, including semantic understanding and knowledge-driven architectures
Enable effective data and analytics governance: Enable policy-driven and context-aware governance, ensuring architecture alignment to the Data strategy and overall technology strategy, principles. Assist data and analytics leaders, and business and IT leadership in developing information governance processes and structures.
Enhance decision making: Use tools such as business information models to provide the organization with a future-state view of the information landscape that is unencumbered by the specific data implementation details imposed by proprietary solutions or technologies.
Consult on business information modeling: Thought leadership and support for creating and managing business information models in all their forms, including conceptual models, relational database designs, message models and others.
Secure data and analytic assets: Aid in the analysis of data and analytics security requirements and solutions, and work with the chief information security officer (CISO) and CDO to ensure that enterprise data and analytics assets are treated as a protected asset.
Data program planning: Ensure that the architecture is used as a lens and a filter to identify, prioritize and execute the data and analytic initiatives with clear line of sight to enterprise strategies and business outcomes.

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