Senior Data Architect - Lima, Peru

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.