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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

• Master’s 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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