Senior Director, Information Management, Data & Analytics

Abbott is a global healthcare leader that helps people live more fully at all stages of life. Our portfolio of life-changing technologies spans the spectrum of healthcare, with leading businesses and products in diagnostics, medical devices, nutritionals and branded generic medicines. Our 115,000 colleagues serve people in more than 160 countries.

     

JOB DESCRIPTION:

Reporting to the DVP of AI, Data, and Automation, the Senior Director of Information Management, Data and Analytics is responsible for our enterprise data and analytics capability. You will own the strategy, lead the team, and architect our pragmatic transition from a fragmented data landscape to a modern, AI-ready foundation, without disrupting what the business depends on today.

You will lead a global technical organization of 50+ FTEs comprising data engineers, architects, BI developers, data product owners, and integration specialists across four interconnected domains: Data Analytics & BI, Data Mesh & Products, Data Platform & Integration, and Governance & Information Management.

What You’ll Work On

Information Management Strategy & Architecture

• Define the multi-year data and analytics strategy aligned with the organizational AI agenda; build investment-backed roadmaps and migration strategies in conjunction with Abbott business units/divisions.
• Establish the architectural blueprint for Information Management, ensuring the organization has the skills and capabilities to design, develop, and deploy cloud-native data fabrics with a pragmatic migration path from legacy foundations, accounting for Abbott’s regulatory requirements. Own the integration and application landscape; make clear decisions on what to build, consolidate, integrate, or retire, and define the organizational structure required to support the strategy.
• Define and enforce data engineering standards across the delivery organization to enable a high-performance organization, including branching strategies, data CI/CD pipelines, automated testing frameworks, and DataOps practices that ensure repeatable, auditable, and high-quality delivery. Stay current with the pace of change in data and AI technology, evaluating emerging standards and translating relevance into pragmatic adoption plans.

Data Platform & Integration

• Lead platform modernization while maintaining critical business operations, ensuring the organization has the capabilities and oversight to design data contracts, integration standards, and observability practices across systems and domains. Ensure the platform architecture supports both batch and real-time data workloads, including event-streaming patterns required for AI inference pipelines and operational analytics, as well as modern database technologies, containerization, and infrastructure-as-code practices..
• Implement MDM disciplines for core entities; build pipeline reliability, SLA tracking, lineage, and automated quality controls, ensuring data observability through suitable tooling.
• Drive application rationalization of the tooling estate for information management, publish standards, negotiate with vendors, and consolidate in support of global application rationalization requirements.

Data Mesh, Data Products & BI

• Drive the shift from pipeline operations to a federated data product model/domain-owned, SLA-governed, documented assets that analytics and AI teams can depend on, built on a self-serve data infrastructure.
• Lead the design and delivery of BI systems and self-service analytics that give business units confidence in the data underlying their decisions, leveraging modern BI platforms (Power BI, Tableau, or equivalent) with governed semantic layers and dataset frameworks.
• Develop KPI frameworks, enterprise reporting standards, and executive dashboards that deliver a consistent, trusted view of the business across divisions.
• Champion the shift from report delivery to analytical product ownership with defined consumers, feedback loops, versioning, and quality SLAs for every data product.

AI Enablement

• Ensure the data estate is AI-ready: clean, accessible, versioned, and governed for model training and inference, including support for LLM fine-tuning, RAG pipelines, and real-time feature serving in partnership with broader members of the BTS organization.
• Build and operate feature stores, real-time pipelines, and vector-enabled datasets supporting both predictive and generative AI use cases, in close partnership with AI Engineering.
• Conduct data readiness assessments before AI investment is committed, scoping use cases against data reality, and maintain a live view of data quality and coverage across priority AI initiatives.