IT Director, Data & AI Architecture

Abbott•Waukegan, IL

3d•$149,300 - $298,700•Hybrid

About The Position

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. 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 serves as Abbott's principal technical authority for enterprise data architecture, cloud data platforms, AI-ready ecosystems, and modern data engineering. This individual is expected to operate as the senior-most data architecture leader, guiding architectural strategy, reviewing solution designs, mentoring architects and engineers, and driving key technology decisions across Abbott's enterprise data landscape. This leader will establish the technical blueprint for Abbott's next-generation data platforms, ensuring scalable, secure, high-performing, and AI-ready architectures that accelerate analytics, automation, and artificial intelligence initiatives across the enterprise.

Requirements

Master's degree in Computer Science, Data Science, Engineering, Information Systems, or a related field.
10+ years of experience in enterprise data architecture, cloud data platform architecture, or large-scale analytics architecture.
5+ years designing and implementing modern cloud-native data platforms.
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 supporting 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 covering metadata, lineage, master data management, and data quality.
Experience leading architecture reviews and providing technical oversight for strategic enterprise initiatives.
Strong communication skills with demonstrated ability to influence senior executives, architects, engineers, and business stakeholders.

Nice To Haves

Experience serving as a Chief Data Architect, Lead Data Architect, Enterprise Data Architect, Principal Architect, or similar senior architecture leadership role.
Experience within 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.
Serve as a trusted technical advisor to 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 related to data 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 that support interoperability, scalability, and AI consumption.
Review and challenge engineering designs to ensure scalability, resiliency, performance, maintainability, and cost optimization.
Partner directly with engineering teams to solve complex technical architecture challenges and accelerate delivery of 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 utilizing Databricks, Delta Lake, Apache Iceberg, Snowflake, and cloud-native storage platforms.
Define architecture standards for data ingestion, transformation, orchestration, observability, DataOps, CI/CD, and platform automation.
Establish patterns supporting structured, semi-structured, streaming, and unstructured data workloads.
Define enterprise integration standards leveraging APIs, event-driven architectures, messaging platforms, and real-time data processing.
Guide implementation of Infrastructure as Code (IaC), 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.
Partner with business and technology leaders to translate strategic priorities into scalable enterprise information architectures.
Architect AI-ready data ecosystems supporting machine learning, predictive analytics, Generative AI, agentic AI, and advanced analytics workloads.
Design reference architectures for Retrieval-Augmented Generation (RAG), semantic search, vector databases, knowledge repositories, and enterprise AI platforms.
Define enterprise 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 architectural standards for feature stores, training datasets, metadata, lineage, and model operationalization.
Evaluate emerging AI technologies and translate them into practical enterprise adoption roadmaps.
Lead AI architecture assessments and provide technical recommendations for strategic AI investments.
Partner with Data Governance and Information Management teams to ensure architectural alignment 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.