A

Abbott

Makes heart devices, glucose sensors and lab tests

IT Director - Data & AI Architecture

Data ManagementConfirmed live in the last 24 hours
Full-time$149.3k - $298.7k

About the Job

Requirements

  • A master's degree in Computer Science, Data Science, Engineering, Information Systems, or a related field is required.
  • At least 10 years of experience in enterprise data architecture, cloud data platform architecture, or large-scale analytics architecture is required.
  • At least 5 years of experience designing and implementing modern cloud-native data platforms and enterprise-scale technology architectures and solutions is required.
  • At least 10 years of overall professional technology experience, with a strong background in enterprise architecture and solution design, is required.
  • At least 3 years at the Director level or in a comparable senior individual-contributor leadership role is required.
  • Demonstrated expertise in technical architecture leadership, including establishing architectural direction and driving strategic technology initiatives, is required.
  • Deep hands-on expertise with Snowflake, Databricks, Microsoft Fabric, Azure Data Services, or equivalent modern data platforms is required.
  • Proven experience designing and implementing large-scale lakehouse architectures is required.
  • Proven experience architecting AI-ready data ecosystems supporting machine learning, Generative AI, vector retrieval, semantic search, and Retrieval-Augmented Generation architectures is required.
  • Deep understanding of Data Mesh, Data Fabric, Data Products, domain-driven design, and modern information architecture principles is required.
  • Experience with enterprise integration patterns, APIs, event-driven architectures, Kafka, streaming platforms, and real-time data processing is required.
  • Experience defining data architecture standards covering metadata, lineage, master data management, and data quality is required.
  • Experience leading architecture reviews and providing technical oversight for strategic enterprise initiatives is required.
  • Experience influencing senior executives, architects, engineers, and business stakeholders is required.

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, continuous integration and continuous delivery, 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, 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 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, 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 the data and AI ecosystem.

Desired Qualifications

  • 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.

About the Company

Abbott

Makes heart devices, glucose sensors and lab tests

Abbott makes medical devices, diagnostic tests, nutrition products and branded generic drugs for hospitals, laboratories, pharmacies and patients. Its best-known product is the FreeStyle Libre, a coin-sized sensor worn on the upper arm that tracks blood sugar around the clock without routine finger pricks. Its other lines run from heart valves, pacemakers and brain stimulators to lab analyzers, rapid tests and mail-in colon cancer screening kits, and Abbott earns money by selling these products to health systems, labs, retailers and consumers. The company was founded in 1888 by Chicago physician Wallace Abbott and is based in Abbott Park, Illinois.

Company size
10,001+
Company stage
IPO
Headquarters
Abbott Park, IL
Founded
1888

Recently Posted Jobs

Explore more opportunities at Abbott

See more jobs