IT Director, Data & AI Architecture

Abbott

Confirmed live 2 days ago High trust

Quick summary

Work type
On-site
Location
Waukegan, ILChicago, IL
Salary
$149,300–$298,700 / yr
Posted
38 days ago
Freshness
Confirmed live 2 days ago

Market check

Salary context

Competitive pay

How this pay compares to similar roles

Similar $236k
This role $224k
$131k most similar roles pay here $317k

This role pays less than 58% of similar roles. Most pay $193,830–$278,000 — the shaded band above. At the midpoint, this role pays about $224k versus about $236k for comparable roles.

Based on 239 similar postings.

Employer

About Abbott

Abbott Laboratories is a global healthcare company that manufactures and markets a broad and diversified line of health care products including diagnostics, medical devices, nutritionals, and branded generic pharmaceuticals. Industry: Healthcare & Medical Devices

Abbott currently has 90 open roles on FindRole.

Listed pay typically runs $99,300–$198,700 across 89 roles with salary data.

Most-posted roles

View all roles at Abbott

At a glance

TL;DR · IT Director, Data & AI Architecture

IT Director, Data & AI Architecture serves as a principal technical authority within the Information Management, Data & Analytics team to define and deliver the enterprise data and AI architecture vision. This individual will architect scalable, secure, and AI-ready data platforms while establishing technical standards for modern data ecosystems. Key responsibilities include leading architecture reviews, defining reference architectures for Snowflake, Databricks, Microsoft Fabric, and Azure Data Services, and overseeing lakehouse designs using Delta Lake and Apache Iceberg. The role involves building infrastructure for RAG, vector databases, and LLMOps to support machine learning and generative AI. Core competencies include expertise in Data Mesh principles, metadata management, and event-driven architectures. This position solves the challenge of transitioning legacy environments into high-performing, interoperable data products that enable advanced analytics and automation across the global healthcare enterprise.

What you'll do

  • Define and lead the enterprise vision for scalable, secure, and AI-ready data architectures.
  • Establish technical standards and reference architectures for Snowflake, Databricks, Microsoft Fabric, and Azure Data Services.
  • Design large-scale lakehouse architectures including medallion designs, semantic layers, and vector databases.
  • Develop architectural standards for data engineering processes such as ingestion, orchestration, CI/CD, and DataOps.
  • Architect AI-ready ecosystems supporting Generative AI, RAG, LLMOps, and advanced machine learning workloads.
  • Define enterprise information architecture including canonical data models, metadata management, and data product standards.
  • Lead architecture review boards to provide final recommendations for critical data and AI investments.
  • Partner with engineering teams to resolve complex technical challenges and ensure high-performing system designs.

What we're looking for

  • 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 of experience designing and implementing modern cloud-native data platforms and enterprise-scale technology architectures.
  • Minimum 10 years of overall professional experience in technology with a focus on enterprise architecture and solution design.
  • Minimum 3 years at the Director level or in a comparable senior individual contributor leadership role.
  • Demonstrated expertise in technical architecture leadership, including establishing architectural direction and driving strategic initiatives.
  • Deep hands-on expertise with Snowflake, Databricks, Microsoft Fabric, Azure Data Services, or equivalent modern data platforms.
  • Proven experience architecting AI-ready ecosystems for machine learning, Generative AI, vector retrieval, and RAG architectures.

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