Sr Mgr Data Management

At Johnson & Johnsonwe believe health is everything. Our strength in healthcare innovation empowers us to build aworld where complex diseases are prevented treated and curedwhere treatments are smarter and less invasive andsolutions are our expertise in Innovative Medicine and MedTech we are uniquely positioned to innovate across the full spectrum of healthcare solutions today to deliver the breakthroughs of tomorrow and profoundly impact health for more at .

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Job Description:

The Senior Manager Data Management provides strategic leadership in shaping Procurement technology data and analytics strategies that improve customer experience reduce cost to serve and increase the efficiency and effectiveness of enterprise solutions. This role sets the vision for data product strategy and governance while leading managers and senior individual contributors to deliver measurable business impact.

Key Responsibilities

Enterprise data strategy: Define and lead the multi-quarter data management roadmap operating model and delivery standards that enable trusted reusable governed and AI-ready data products.

Data product leadership: Own the lifecycle of enterprise data products across priority domains including requirements business rules modeling documentation refresh cadence change control adoption and value realization.

Modern data architecture: Guide scalable ingestion transformation semantic-ready modeling orchestration and platform patterns across modern data platforms such as Databricks Microsoft Fabric cloud storage and related enterprise technologies.

AI readiness: Build the data foundation required for advanced analytics GenAI conversational analytics vector search knowledge graphs and agentic use cases by ensuring data is curated traceable accessible secure and fit for purpose.

Governance and stewardship: Establish governance practices for metadata lineage cataloging data quality access controls privacy security responsible AI controls ownership and stewardship across critical data assets.

Production operations: Define and manage service levels for data products and pipelines including monitoring observability incident response root-cause analysis release governance performance management and continuous improvement.

Stakeholder partnership: Translate business priorities into durable data capabilities by partnering with analytics technology process security privacy governance and business leaders to align roadmaps resolve tradeoffs and deliver measurable outcomes.

People and capability leadership: Lead managers senior individual contributors and cross-functional delivery partners while building talent raising technical standards strengthening AI/data fluency and creating a high-performing data management culture.

Qualifications

• Masters degree in Computer Science Information Systems Data Engineering Analytics AI or a related field; equivalent experience may be considered.
• 10 years of progressive experience in data management data engineering enterprise data platforms AI-ready data products or advanced analytics enablement including team leadership or complex cross-functional initiative ownership.
• Demonstrated ability to define and execute enterprise data management strategies operating models roadmaps and delivery standards that enable scalable governed reusable AI-ready data products.
• Strong understanding of modern data architecture data modeling ETL/ELT orchestration SQL cloud storage and compute semantic layers vector search knowledge graphs and platforms such as Databricks Microsoft Fabric Azure AI Search or comparable enterprise data platforms.
• Proven experience implementing data governance data quality metadata management lineage observability access controls stewardship privacy security and responsible AI controls in a regulated or enterprise-scale environment.
• Experience managing production data products and AI-enabling assets with defined service levels including monitoring incident response release governance performance management documentation adoption validation and continuous improvement.
• Ability to translate business priorities into durable data and AI capabilities while partnering with analytics technology process security privacy governance and business stakeholders; strong executive communication skills required.

Preferred Skills

Experience applying DataOps and MLOps practices including version control automated testing release automation model/data monitoring and controlled promotion across environments.

Exposure to agentic AI retrieval-augmented generation prompt engineering vector databases or AI workflow orchestration to improve data stewardship metadata enrichment and insight discovery.

Ability to evaluate emerging data and AI technologies shape proofs of concept define success criteria and convert promising capabilities into scalable enterprise practices.

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