IT Director, Data & AI Architecture
About The Position
Abbott is seeking a senior hands-on technical leader to define and deliver its enterprise data and AI architecture vision. Reporting to the Director of Information Management, Data & Analytics, this role serves as Abbott’s principal technical authority for enterprise data architecture, cloud data platforms, AI-ready ecosystems, and modern data engineering. The position establishes the technical blueprint for scalable, secure, high-performing data platforms that support analytics, automation, and AI initiatives across the enterprise. Rather than serving primarily as a people manager, this leader acts as a trusted advisor and go-to expert for complex architectural decisions. The role guides architecture strategy, reviews and challenges solution designs, mentors architects and engineers, influences technology roadmaps, and provides recommendations on major data, analytics, and AI investments. It works with engineering, infrastructure, platform, governance, information management, business, and technology teams to ensure architectures align with business objectives and requirements for scalability, innovation, security, trust, and regulatory compliance. The role covers enterprise information architecture, cloud data platforms, lakehouse and data product architectures, data engineering and integration standards, and architectures for machine learning, Generative AI, agentic AI, and advanced analytics. Within the first 12 months, the leader is expected to establish Abbott’s target-state Data & AI Architecture and modernization roadmap, define enterprise standards, accelerate legacy modernization while maintaining operational stability, increase adoption of reusable and trusted data assets, and enable capabilities that support business outcomes.
Requirements
- Master’s degree in Computer Science, Data Science, Engineering, Information Systems, or a related field.
- 10 or more years of experience in enterprise data architecture, cloud data platform architecture, or large-scale analytics architecture.
- 5 or more years designing and implementing modern cloud-native data platforms and enterprise-scale technology architectures and solutions.
- At least 10 years of overall professional technology experience, with a strong background in enterprise architecture and solution design.
- At least 3 years at the Director level or in a comparable senior individual-contributor leadership role.
- Demonstrated technical architecture leadership, including establishing architectural direction and driving strategic technology initiatives.
- Deep hands-on expertise with Snowflake, Databricks, Microsoft Fabric, Azure Data Services, or equivalent modern data platforms.
- Proven experience designing and implementing large-scale lakehouse architectures.
- Proven experience architecting AI-ready data ecosystems for machine learning, Generative AI, vector retrieval, semantic search, and RAG architectures.
- Deep understanding of Data Mesh, Data Fabric, Data Products, domain-driven design, and modern information architecture principles.
- Experience with enterprise integration patterns, APIs, event-driven architectures, Kafka, streaming platforms, and real-time data processing.
- Experience defining data architecture standards for metadata, lineage, master data management, and data quality.
- Experience leading architecture reviews and providing technical oversight for strategic enterprise initiatives.
- Strong communication skills and demonstrated ability to influence senior executives, architects, engineers, and business stakeholders.
Nice To Haves
- Experience as a Chief Data Architect, Lead Data Architect, Enterprise Data Architect, Principal Architect, or in a similar senior architecture leadership role.
- Experience in healthcare, medical devices, life sciences, pharmaceuticals, or regulated manufacturing environments.
- Hands-on expertise with Databricks, Snowflake, Microsoft Fabric, Azure, Kubernetes, Apache Airflow, Delta Lake, Apache Iceberg, Kafka, and related cloud-native technologies.
- Experience with vector databases, knowledge graphs, semantic search platforms, and enterprise AI platforms.
- Relevant certifications in Cloud Architecture, Data Engineering, AI Engineering, Enterprise Architecture, or related disciplines.
Responsibilities
- Lead architecture reviews for major data and analytics initiatives.
- Advise engineering, architecture, and business leaders on enterprise data strategy and architecture decisions.
- Define reference architectures and implementation standards for Snowflake, Databricks, Microsoft Fabric, Azure Data Services, and related cloud technologies.
- Drive architectural decisions concerning lakehouse design, medallion architectures, semantic layers, metadata services, data observability, vector databases, and enterprise AI platforms.
- Define enterprise information architecture, canonical data models, domain ownership boundaries, and data product standards.
- Review and challenge engineering designs for scalability, resiliency, performance, maintainability, and cost optimization.
- Partner with engineering teams to resolve complex architecture challenges and accelerate strategic initiatives.
- Chair architecture review boards and provide final architecture recommendations for critical data, analytics, and AI investments.
- Maintain hands-on awareness of modern data engineering, cloud, analytics, and AI technologies.
- Design enterprise-scale lakehouse architectures using Databricks, Delta Lake, Apache Iceberg, Snowflake, and cloud-native storage platforms.
- Define standards for data ingestion, transformation, orchestration, observability, DataOps, CI/CD, and platform automation.
- Establish patterns for structured, semi-structured, streaming, and unstructured data workloads.
- Define integration standards using APIs, event-driven architectures, messaging platforms, and real-time data processing.
- Guide implementation of Infrastructure as Code, platform engineering, containerization, and automated deployment practices.
- Partner with infrastructure and platform teams to optimize performance, reliability, scalability, and cost management across enterprise data platforms.
- Define enterprise standards for data products, data contracts, metadata management, discoverability, interoperability, and lifecycle management.
- Drive implementation of Data Mesh and federated data ownership principles across Abbott business domains.
- Establish architecture patterns that enable reusable, trusted, and scalable data assets.
- Translate strategic priorities into scalable enterprise information architectures in partnership with business and technology leaders.
- Architect AI-ready data ecosystems for machine learning, predictive analytics, Generative AI, agentic AI, and advanced analytics.
- Design reference architectures for Retrieval-Augmented Generation, semantic search, vector databases, knowledge repositories, and enterprise AI platforms.
- Define approaches for embeddings, vector storage, semantic retrieval, knowledge management, and AI-ready data foundations.
- Establish LLMOps and MLOps standards for model deployment, monitoring, observability, governance, and lifecycle management.
- Define architecture standards for feature stores, training datasets, metadata, lineage, and model operationalization.
- Evaluate emerging AI technologies and develop practical enterprise adoption roadmaps.
- Lead AI architecture assessments and recommend technical approaches for strategic AI investments.
- Partner with Data Governance and Information Management teams to align architecture with metadata, lineage, master data, data quality, privacy, security, and regulatory requirements.
- Define architectural controls that enable trusted, auditable, and governed enterprise data.
- Promote trust-by-design principles throughout Abbott’s data and AI ecosystem.
- Establish Abbott’s target-state enterprise Data & AI Architecture and modernization roadmap within the first 12 months.
- Define enterprise standards for data products, lakehouse architecture, AI-ready datasets, integration, and platform design.
- Modernize legacy data environments while maintaining operational stability.
- Increase adoption of reusable, trusted, and scalable enterprise data assets.
- Enable scalable AI, analytics, and automation capabilities that support business outcomes.
- Improve interoperability, architectural consistency, data quality, and platform performance across Abbott’s global ecosystem.
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What This Job Offers
Job Type
Full-time
Career Level
Director
