IT Director, Data & AI Architecture

Abbott•Chicago, IL

4d•$149,300 - $298,700•Onsite

About The Position

The IT Director, Data & AI Architecture is Abbott’s senior technical authority for enterprise data architecture, cloud data platforms, AI-ready ecosystems, and modern data engineering. Reporting to the Director of Information Management, Data & Analytics, this senior hands-on technical leader defines and delivers Abbott’s enterprise Data & AI architecture vision and technical blueprint, establishing scalable, secure, high-performing platforms and standards that support analytics, automation, and AI initiatives. The role is an individual-contributor leadership position rather than a people-management role, serving as a trusted advisor and primary expert for complex architectural decisions across the enterprise. The leader guides architectural strategy, reviews and challenges solution designs, mentors architects and engineers, chairs architecture review boards, and influences technology decisions and roadmaps. They define standards and reference architectures for cloud data platforms, data engineering, enterprise integration, data products, information architecture, governance, and AI/ML workloads. The role works with engineering, architecture, infrastructure, governance, business, and platform teams to modernize Abbott’s data ecosystem and translate strategic priorities into enterprise-scale solutions. Within the first 12 months, success includes establishing the target-state enterprise Data & AI architecture and modernization roadmap; defining standards for data products, lakehouse architecture, AI-ready datasets, integration, and platform design; accelerating legacy modernization while maintaining operational stability; increasing adoption of reusable and trusted data assets; enabling business-focused AI, analytics, and automation; and improving interoperability, consistency, data quality, and platform performance across Abbott’s global ecosystem.

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 and enterprise-scale technology architectures and solutions.
At least 10 years of overall professional experience in technology, with a strong background in enterprise architecture and solution design.
At least 3 years at 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 and provide technical oversight for strategic enterprise work.
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 architecture decisions for lakehouse and 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 enterprise 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 data platform performance, reliability, scalability, and cost.
Define standards for data products, data contracts, metadata management, discoverability, interoperability, and lifecycle management.
Drive Data Mesh and federated data ownership principles across Abbott business domains.
Establish patterns that enable reusable, trusted, and scalable data assets.
Work with business and technology leaders to translate priorities into scalable enterprise information architectures.
Architect AI-ready data ecosystems for machine learning, predictive analytics, Generative AI, agentic AI, and advanced analytics.
Design reference architectures for 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 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 for trusted, auditable, and governed enterprise data and promote trust-by-design principles across the data and AI ecosystem.
Mentor architects and engineers and influence enterprise technology and architecture decisions.