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 Stellus Rx will be a key member of our Technology Team, working closely with Stellus Rx 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 Stellus Rx.
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 data infrastructure trends, and industry best practices; provide recommendations on adoption timing and implementation approach.
Qualifications and Requirements:
- 7+ years of experience in data architecture, data engineering, or a closely related field.
- 3+ years of experience designing enterprise-scale data platforms in cloud environments (AWS strongly preferred).
- Required: Demonstrated, hands-on experience using AI tools to accelerate data architecture design, automate data quality, or enable AI/ML workloads — with specific examples you can speak to.
- Deep expertise in data modeling techniques including dimensional modeling, data vault, and lakehouse patterns.
- Strong knowledge of ELT/ETL pipeline architecture and workflow orchestration (Airflow or similar).
- Experience with cloud data platforms such as AWS Redshift, S3, Glue, Athena, or equivalents.
- Proficiency in SQL and at least one scripting language (Python preferred).
- Experience with relational and NoSQL databases; familiarity with vector databases a plus.
- Strong understanding of data governance, data quality frameworks, and MDM principles.
- Familiarity with healthcare data compliance requirements (HIPAA, SOC 2).
- Excellent communication skills with the ability to convey complex architectural concepts to technical and non-technical audiences.
- Bachelor's or graduate degree in Computer Science, Information Systems, Statistics, or a related quantitative field.
- High English proficiency, written and verbal.
Preferred Experience:
- Hands-on experience designing data infrastructure for AI/ML workloads, including feature stores, vector databases, or RAG pipelines.
- Familiarity with AI-powered data observability platforms (e.g., Monte Carlo, Soda, or similar).
- Experience with healthcare data standards including FHIR and HL7.
- Experience with real-time streaming architectures (Kafka, Kinesis, or similar).
- Relevant certifications: AWS Certified Data Analytics, AWS Solutions Architect, or DAMA CDMP.
- Bilingual — Spanish and English.
- MBA or advanced degree.
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