Senior Data Architect

Job Summary We're opening eyes, hearts and minds to the impact that a pharmacy team can have in changing lives. Join our group of talented, committed team members-pharmacists, pharmacy care coordinators, technologists, product strategists and more-to create and expand the delivery of personalized health support that people didn't even know could be possible. The Senior Data Architect for Lumen Dynamics will be a key member of our Technology Team, working closely with Lumen Dynamics leaders and across the organization to unlock the health of millions of Americans. We are a culture that is unabashedly driven by purpose — making a difference to patients and team members while growing at an accelerated rate. This role is built for a data architect who actively uses AI to design smarter data systems, accelerate architectural decision-making, and build the data foundations that enable AI and machine learning to thrive across the organization — rather than treating AI as an afterthought in the data stack. Role and Responsibilities: AI-Informed Data Architecture Design Define and maintain enterprise data architecture standards across structured, semi-structured, and unstructured data domains — with deliberate design for AI/ML workloads, including feature stores, vector databases, and embedding pipelines. Use AI-assisted modeling tools to accelerate data model design, evaluate architectural trade-offs, and validate designs against business requirements before committing to implementation. Design and govern the organization's cloud data lake, data warehouse, and lakehouse architectures on AWS — ensuring they are optimized for both analytical and AI/ML consumption patterns. Establish data ontology, taxonomy, and semantic layer standards that enable AI systems to reason over organizational data accurately and consistently. Evaluate emerging data architecture patterns — including retrieval-augmented generation (RAG), real-time feature serving, and vector search — and build a roadmap for their adoption across Lumen Dynamics . AI-Ready Data Modeling & Pipeline Architecture Design scalable data models and ELT/ETL pipeline architectures that support both traditional analytics and AI/ML model training and inference workloads. Use AI code generation tools to accelerate the authoring and validation of data models, transformation logic, and pipeline configurations — replacing manual, repetitive design work with intelligent, AI-assisted development. Define standards for data partitioning, indexing, caching, and storage optimization; use AI-driven performance analysis to continuously validate and improve architectural decisions. Partner with Data Engineers to translate architectural blueprints into production-ready pipelines, providing hands-on guidance and AI-augmented design reviews. Data Governance, Quality & Compliance Define and enforce data governance frameworks, data quality standards, and data contracts across the enterprise — using AI-powered data observability tools to automate quality monitoring and surface issues proactively rather than through manual review. Ensure data architecture meets compliance requirements relevant to healthcare (HIPAA, SOC 2, NIST); use AI-assisted compliance tooling to continuously monitor for policy drift and streamline audit evidence generation. Develop and maintain a master data management (MDM) strategy that ensures consistency, accuracy, and trustworthiness of critical data assets across systems. Champion data privacy and security principles in architectural design, including data lineage tracking, access controls, and anonymization strategies for sensitive healthcare data. AI & Analytics Enablement Design data infrastructure that serves as the foundation for AI/ML initiatives — ensuring data is accessible, well-labeled, versioned, and structured to support model training, validation, and ongoing inference at scale. Collaborate with data scientists and ML engineers to understand modeling requirements and translate them into data architecture decisions that reduce friction in the AI development lifecycle. Use AI-assisted analysis to identify high-value data assets that are underutilized, and develop strategies to unlock their potential for analytics and AI-driven decision-making. Partner with Business Intelligence and Product teams to ensure the data architecture supports self-service analytics, real-time dashboards, and AI-powered reporting capabilities. Standards, Documentation & Team Enablement Define and maintain data architecture standards, patterns, and best practices across the organization; use AI tools to generate, review, and keep documentation current with minimal manual overhead. Mentor Data Engineers and Analysts, guiding them in applying architectural standards and AI-augmented data development practices. Communicate architectural decisions, trade-offs, and roadmap recommendations clearly to both technical teams and executive leadership. Stay current on emerging data technologies, AI/ML