Senior Director, AI, Data & Architecture

Role Description

The Senior Director, AI, Data, and Architecture is responsible for leading EnlivenHealth’s AI strategy, data platform direction, enterprise architecture, and technical modernization across the Product and Engineering organization. This role serves as a senior technical leader responsible for helping EnlivenHealth responsibly apply AI to improve product capabilities, engineering productivity, analytics, automation, and customer outcomes.

The Director will define practical AI patterns, data foundations, architecture standards, and modernization roadmaps that enable scalable, secure, reliable, and innovative healthcare technology solutions. The Senior Director partners closely with various stakeholders to translate business priorities into executable AI, data, integration, and architecture plans.

Key Responsibilities

• AI Strategy and Enablement
  • Define and lead EnlivenHealth’s AI strategy across Product and Engineering.
  • Identify, prioritize, and guide AI use cases that improve patient engagement, clinical workflows, financial operations, analytics, automation, and internal productivity.
  • Establish AI architecture patterns, development standards, lifecycle practices, evaluation methods, and guardrails for responsible AI adoption.
  • Partner with Product leaders to develop customer-facing AI capabilities that create measurable value while respecting healthcare privacy, security, explainability, and customer trust requirements.
  • Partner with Engineering and Quality teams to expand AI-assisted software development, test automation, documentation, code review, knowledge management, and operational workflows.
  • Track AI adoption, productivity impact, quality impact, risks, and lessons learned.
  • Ensure AI initiatives align with Omnicell and EnlivenHealth policies, customer commitments, data-use obligations, and applicable regulatory expectations.
• Data Platform Strategy and Governance
  • Define the strategic direction for EnlivenHealth’s data platform, including data architecture, data flows, data quality, data governance, analytics enablement, and AI readiness.
  • Support the development of a broader patient, pharmacy, financial, clinical, and outcomes data foundation.
  • Establish practical standards for data ingestion, transformation, storage, lineage, access, quality, retention, and security.
  • Partner with Security, Privacy, Legal, Compliance, Product, and Engineering stakeholders to ensure healthcare data is handled appropriately.
  • Identify opportunities to consolidate data assets, reduce duplication, and create trusted data products.
• Enterprise and Solution Architecture
  • Define and maintain enterprise architecture principles, standards, decision practices, and reference patterns for EnlivenHealth platforms.
  • Lead solution architecture for major product initiatives, platform modernization, integrations, AI-enabled capabilities, and data-driven services.
  • Partner with Product and Engineering leaders to ensure architecture decisions align with customer needs, scalability, security, reliability, interoperability, and operating efficiency.
  • Identify technical debt, architectural risk, platform fragmentation, and scalability constraints; develop practical plans to address them.
• Modernization, API, and Integration Architecture
  • Own and maintain the technical modernization roadmap in partnership with Product, Engineering, Platform, Security, and Cloud Operations.
  • Drive patterns that simplify platforms, reduce operational risk, improve resiliency, and enable faster product delivery.
  • Define API and integration architecture standards, including authentication, authorization, versioning, monitoring, documentation, and lifecycle management.
  • Support scalable integration with pharmacy management systems, health systems, payers, partners, and other healthcare ecosystem participants.

Qualifications

• 10+ years of progressive experience in software engineering, enterprise architecture, solution architecture, data architecture, cloud architecture, AI enablement, or related technology leadership roles.
• 5+ years leading architecture, technical strategy, data platform, AI, or modernization initiatives across complex SaaS or enterprise software environments.
• Practical experience with AI technologies and implementation patterns, including generative AI, machine learning concepts, responsible AI practices, model evaluation, prompt/workflow design, and AI-enabled software delivery.
• Experience defining AI standards, architecture roadmaps, technical patterns, integration approaches, and modernization strategies.
• Strong understanding of cloud-native architecture, distributed systems, APIs, data platforms, security patterns, observability, resiliency, and scalable SaaS operations.
• Experience with data architecture, data governance, analytics enablement, and modern data platform concepts.
• Strong knowledge of healthcare data privacy, security, compliance, and customer trust considerations, including HIPAA, SOC 2, HITRUST, or similar control environments.
• Excellent communication skills with the ability to explain complex technical tradeoffs to technical, executive, customer, and non-technical audiences.
• Proven ability to balance innovation, execution, security, operational reliability, and long-term platform health.

Preferred Qualifications

• Bachelor’s degree in Computer Science, Engineering, Information Systems, Data Science, or related field.
• Master’s degree or MBA with a technology, data, AI, or healthcare focus.
• Experience in healthcare technology, pharmacy technology, patient engagement, clinical workflow, claims, or financial technology.
• Experience with AWS cloud services, AI platforms, event-driven architecture, containerized platforms, serverless technologies, and cloud-native modernization.
• Experience with AI platforms and services such as AWS Bedrock, Kiro, OpenAI, Anthropic Claude, GitHub Copilot, or comparable technologies.
• Experience establishing AI governance, responsible AI review processes, model evaluation practices, and secure AI development patterns.