IT Director, Data & AI Architecture

About Abbott

Abbott is a global healthcare leader that helps people live more fully at all stages of life. Our portfolio of life-changing technologies spans diagnostics, medical devices, nutrition, and branded generic medicines. With 115,000 colleagues serving people in more than 160 countries, Abbott is committed to advancing healthcare through innovation, data, and technology.

The Opportunity

Reporting to the Director of Information Management, Data & Analytics, the Data & AI Architect will play a critical role in defining and delivering Abbott's enterprise data and AI architecture vision. This leader will architect scalable, secure, and AI-ready data platforms, enable advanced analytics and AI use cases, and establish the technical standards that support Abbott's transition to a modern data ecosystem.

The Data & AI Architect will serve as the principal technical authority across data platforms, data products, analytics, integration patterns, and AI enablement capabilities. Working closely with Data Engineering, AI Engineering, Enterprise Architecture, Security, and business stakeholders, this role will ensure Abbott's data estate supports current operational needs while accelerating innovation in analytics, automation, and artificial intelligence.

What You'll Work On

Enterprise Data & AI Architecture

• Define and maintain the enterprise data and AI reference architecture aligned with Abbott's Information Management and AI strategy.
• Develop future-state architecture blueprints supporting Data Mesh, Data Fabric, cloud-native analytics platforms, and AI-enabled business capabilities.
• Establish architecture principles, standards, patterns, and governance frameworks for enterprise data and AI solutions.
• Partner with Enterprise Architecture to ensure alignment between business, application, data, and technology architectures.
• Evaluate emerging technologies, industry trends, and AI capabilities and translate them into practical adoption roadmaps.

Data Platform Architecture

• Design scalable and secure cloud-native data platforms leveraging technologies such as Snowflake, Databricks, Microsoft Fabric, Azure Data Services, and related ecosystem tools.
• Define architecture standards for ingestion, transformation, storage, orchestration, observability, metadata management, and data sharing.
• Establish design patterns supporting structured, semi-structured, streaming, and unstructured data workloads.
• Define architectural approaches for Infrastructure-as-Code, platform automation, containerization, and CI/CD enablement.
• Partner with platform and engineering teams to optimize scalability, resiliency, performance, and cost management.

Data Integration & Data Products

• Define enterprise integration patterns supporting batch, near real-time, API-driven, and event-driven architectures.
• Establish standards for data contracts, domain ownership, interoperability, and Data Product lifecycle management.
• Drive implementation of reusable data assets and federated Data Mesh principles across Abbott business domains.
• Ensure data products are discoverable, documented, governed, and aligned with business and AI consumption requirements.
• Partner with business units to translate strategic priorities into scalable information architectures.

AI & Advanced Analytics Enablement

• Architect AI-ready data ecosystems supporting machine learning, generative AI, predictive analytics, and agent-based solutions.
• Design foundational capabilities including feature stores, vector databases, semantic layers, knowledge repositories, and Retrieval-Augmented Generation (RAG) architectures.
• Establish architectural standards for model training data, feature engineering, metadata, lineage, and model operationalization.
• Collaborate with AI Engineering and Automation teams to ensure secure and scalable integration of AI services into enterprise workflows.
• Lead data readiness and AI architecture assessments for strategic AI investments and initiatives.

Cloud & Digital Platform Strategy

• Partner with Cloud Infrastructure, Enterprise Architecture, and Security leaders to define and evolve Abbott's hybrid-cloud and multi-cloud strategy, ensuring alignment with enterprise technology, data, and AI objectives.
• Guide enterprise adoption of cloud-native architecture patterns, Infrastructure as Code (IaC), DevOps, Platform Engineering, and automated operational practices that improve scalability, reliability, and delivery velocity.
• Drive technology simplification, platform rationalization, and modernization initiatives by identifying opportunities to consolidate legacy technologies, reduce technical debt, improve supportability, and optimize total cost of ownership.
• Ensure cloud platform architectures are designed to support modern analytics, AI, automation, and business-critical workloads while maintaining resiliency, performance, security, and compliance standards.

Data Governance & Information Management

• Partner with Governance and Information Management teams to establish metadata, lineage, data quality, and master data architecture standards.
• Ensure architectures support Abbott's regulatory, privacy, security, and compliance requirements.
• Define controls that enable trusted, auditable, and governed data across the enterprise.
• Support implementation of enterprise data catalogs, business glossaries, lineage solutions, and stewardship processes.
• Promote "trust by design" principles throughout the data and AI ecosystem.

Technology Leadership & Delivery

• Provide architecture leadership for major strategic initiatives, ensuring alignment with enterprise standards and business outcomes.
• Conduct architecture reviews and approve solution designs for critical programs.
• Mentor engineers, architects, and technical teams in modern data management and AI engineering practices.
• Influence investment decisions, vendor evaluations, and platform roadmap development.
• Support program teams throughout the delivery lifecycle, ensuring technical quality, scalability, and long‑term maintainability.

Key Responsibilities

• Own enterprise-wide data and AI architecture standards and roadmaps.
• Serve as lead architect for strategic data platform modernization initiatives.
• Drive architectural governance across analytics, integration, data products, and AI solutions.
• Define technology standards and reference implementations supporting cloud-first architectures.
• Partner with Security, Infrastructure, Enterprise Architecture, and AI Engineering teams to ensure alignment and compliance.
• Lead proof-of-concepts and technology evaluations for new data and AI capabilities.
• Act as a trusted advisor to senior business and technology leadership.

Success Measures

Within the first 12-24 months, the Data & AI Architect will:

• Establish Abbott's target-state Data & AI Architecture and roadmap.
• Define enterprise standards for Data Products, AI-ready datasets, integration, and governance.
• Accelerate modernization of legacy data environments while maintaining operational stability.
• Increase adoption of reusable, governed, and trusted data assets across the enterprise.
• Enable scalable AI and analytics capabilities that directly support business outcomes.
• Improve platform interoperability, data quality, and architectural consistency across Abbott's global landscape.

Required Qualifications

• Master's degree in Computer Science, Data Science, Information Systems, Engineering, or related field.
• 10+ years of experience in enterprise data architecture, data engineering, analytics architecture, or cloud platform architecture.
• 5+ years designing and implementing enterprise-scale cloud data platforms.
• Demonstrated experience architecting modern data ecosystems using technologies such as Snowflake, Databricks, Microsoft Fabric, Azure Synapse, Azure Data Lake, or equivalent platforms.
• Experience designing architectures supporting AI/ML, Generative AI, RAG, feature stores, and advanced analytics workloads.
• Strong understanding of Data Mesh, Data Fabric, Data Products, and modern information management principles.
• Deep knowledge of enterprise integration patterns, APIs, event-streaming platforms, and real‑time data architectures.
• Experience implementing data governance, metadata management, lineage, MDM, and data quality solutions.
• Strong understanding of regulatory and compliance considerations within healthcare, medical device, pharmaceutical, or highly regulated industries.
• Excellent stakeholder management, communication, and influencing skills.

Preferred Qualifications

• Experience within healthcare, life sciences, medical devices, pharmaceuticals, or regulated manufacturing industries.
• Hands‑on expertise with Snowflake, Databricks, Microsoft Fabric, Azure, Kubernetes, Apache Airflow, Delta Lake, Apache Iceberg, Kafka, or similar technologies.
• Experience with vector databases, semantic search platforms, knowledge graphs, and enterprise AI platforms.
• Relevant certifications in Azure, Cloud Architecture, Data Engineering, AI Engineering, or Enterprise Architecture.
• Experience supporting global organizations and large-scale digital transformation programs.

The base pay for this position is $149,300.00 - $298,700.00. In specific locations, the pay range may vary from the range posted.