Senior Manager, S2P Analytics & Master Data

Job Description

Role Summary

The Senior Manager S2P Analytics & Master Data serves as the business owner for the Source-to-Pay (S2P) data and analytics foundation. This role ensures that supplier taxonomy/category preferred supplier item/service contract risk and related reference data are accurate governed scalable and fit for purposewhile translating that data into trusted insights executive narratives self-service analytics automation and AI-enabled decision support.

The role leads the business-side governance model master-data lifecycle data quality standards analytics product portfolio insight-generation capability and enabling platform requirements across the S2P lifecycle. It partners closely with S2C and P2P Process & Platform Enablement Strategy/Governance/Excellence SBE Finance Legal Risk IT/Digital enterprise Data Governance and S2P Transformation teams.

This leader makes data a trusted business asset that powers reliable operations decision-grade reporting controls intelligent routing automation performance management and future-state AI capabilities.

Key Responsibilities

1. S2P Master Data Taxonomy & Governance Ownership

• Serve as the senior business lead for S2P data domains including supplier master third-party entity types taxonomy/category structures preferred supplier designation item/service data contract/reference data risk attributes and related reference data.
• Own business-led governance standards for data ownership stewardship approval workflows change control escalation paths and decision rights across Procurement Finance Legal Risk IT/Digital and enterprise data teams.
• Establish data definitions semantic standards lineage expectations quality requirements and governance routines that enable consistent reporting automation analytics and AI use cases.
• Ensure taxonomy and master-data standards support buying-channel guidance preferred supplier utilization supplier segmentation risk screening compliance monitoring routing value tracking and performance analysis.

2. Master Data Lifecycle Process & Platform Enablement

• Lead business requirements and lifecycle processes for data creation validation enrichment maintenance synchronization change control retirement exception management and remediation.
• Embed data management requirements into S2C P2P TPRM CLM intake/orchestration and buying-channel workflows rather than treating data management as a separate manual activity.
• Partner with IT/Digital and platform owners to define requirements for MDM Coupa Icertis ProcessUnity Zip VDP/Snowflake Power BI and related integrations.
• Drive standardization integration and automation to reduce rework duplicate maintenance manual spreadsheets data defects and point-to-point workarounds.

3. Analytics Product Ownership & Decision Support

• Own the S2P analytics product portfolio including executive dashboards KPI views category and program dashboards standard performance scorecards self-service analytics and automated insight products.
• Define and maintain an integrated data and analytics roadmap based on business priorities stakeholder needs technology maturity data risk automation dependency and transformation value.
• Ensure each analytics product has a defined audience business purpose trusted KPI set data source adoption plan and operating cadence.
• Translate data into clear executive narratives that explain what changed why it matters and what decisions or actions are required.
• Generate decision-ready insights across spend sourcing suppliers P2P performance value delivery savings visibility compliance cycle times preferred supplier utilization supplier risk and SBE visibility.

4. Data Quality Controls Compliance & Risk Enablement

• Define data-quality rules validation logic exception thresholds monitoring routines and remediation playbooks across S2P master-data and analytics domains.
• Partner with Compliance Risk Finance Internal Audit TPRM Sourcing and Procurement Operations to ensure data supports regulatory expectations audit readiness financial controls segregation of duties risk visibility and policy adherence.
• Reduce operational compliance and reporting risk resulting from incomplete inconsistent duplicative or poorly governed data.

5. Analytics Automation & AI Enablement

• Lead the business roadmap for data and analytics automation including data validation enrichment deduplication exception detection stewardship workflows automated insights conversational analytics and agent-enabled reporting.
• Partner with IT/Digital enterprise Data Governance and data-platform teams to establish AI-ready data foundations including governed definitions semantic consistency lineage quality controls access standards and Snowflake/VDP readiness.
• Evaluate and enable capabilities such as AI agents natural-language analytics automated insight generation exception-based reporting and self-service decision support.
• Identify opportunities to reduce manual reporting and data-cleanup effort shifting capacity toward performance dialogue insight generation governance and scalable capability building.

6. Intake Operating Model & Adoption

• Design and lead a sustainable operating model for S2P data stewardship analytics intake prioritization backlog management issue resolution and delivery governance.
• Establish predictable routines for data-quality monitoring taxonomy changes dashboard refreshes performance reviews stakeholder feedback and roadmap decisions.
• Define clear accountability across Procurement Operations Sourcing Finance Legal Risk IT/Digital enterprise Data Governance and offshore or managed-service support teams.
• Drive adoption through standards documentation playbooks training office hours stakeholder engagement and clear role definitions.
• Differentiate and spearhead the strategic ownership of data governance analytics products insight delivery and enablement from the day-to-day report production dashboard maintenance transactional execution and operational data cleanup in parallel.

7. Cross-COE & Cross-Platform Collaboration

• Partner with P2P Process & Platform Enablement to ensure supplier item/service and reference data support clean transaction execution control integrity and operational performance visibility.
• Partner with S2C Process & Platform Enablement to ensure sourcing and contracting activities create high-quality downstream data and enable sourcing-performance analytics.
• Partner with Strategy Governance & Excellence SBE TPRM and Supplier Performance leaders to support trusted metrics and visibility into savings value delivery SBE performance risk supplier segmentation and program outcomes.
• Coordinate with IT/Digital enterprise Data Governance MDM VDP/Snowflake and platform teams on architecture integrations access data flows tooling alignment and long-term scalability.

Ownership Boundaries

This role owns:

• Business governance standards roadmap requirements and operating model for S2P master data taxonomy reference data analytics products insight delivery and AI-enabled analytics.
• Data-quality frameworks stewardship routines analytics intake analytics-product adoption and business requirements for data and analytics platforms.
• Strategic level oversight of Day-to-day operational report production or routine dashboard maintenance

This role does not Own:

• Transaction execution sourcing execution or supplier relationship strategy.
• Savings methodology target setting or SBE program ownership.
• Policy definition legal determinations or enterprise technology delivery.
• The role partners with accountable process program finance governance supplier and platform owners to ensure their capabilities use consistent trusted and actionable data.

Qualifications & Experience

Required:

• 8 years of experience in master data management data governance procurement analytics spend analytics sourcing/procurement insights business intelligence procurement operations finance supply chain or Source-to-Pay environments.
• Strong understanding of how supplier taxonomy contract risk preferred supplier and reference data affect S2P execution controls analytics automation reporting and decision-making.
• Demonstrated experience defining business-side data-governance models including ownership stewardship quality standards approval workflows change control and escalation routines.
• Experience building executive dashboards KPI frameworks operational scorecards analytics roadmaps and decision-support narratives.
• Ability to translate business needs into practical data process platform integration reporting automation and AI-readiness requirements in partnership with IT/Digital and enterprise data teams.

Preferred:

• Experience enabling supplier MDM taxonomy/category structures preferred supplier data supplier segmentation third-party entity types CLM TPRM or related data dependencies at scale.
• Experience improving data quality through process redesign platform enablement integration automation and operating-model change.
• Experience enabling self-service analytics automated insights AI-assisted reporting conversational analytics or agent-enabled capabilities.
• Familiarity with Coupa Icertis ProcessUnity Zip MDM or MDG platforms Data Lakes (Snowflake) Power BI or comparable procurement and data ecosystems.
• Experience in a global COE shared-service transformation enterprise-governance or digitally enabled procurement environment.

Leadership Attributes

Business-oriented data and analytics leader who views data as a strategic enabler of execution insight controls automation user experience and AI readiness.

Insight-driven decision partner who focuses on actions performance outcomes and credible executive storytellingnot simply data availability.

Systems thinker who connects data design to process flows platform behavior reporting outputs risk controls and user adoption.

Strong cross-functional influencer who can advance standards stewardship accountability and adoption through partnership rather than direct authority.

Future-oriented capability builder who can move the organization from manual reporting and fragmented data management to scalable data products automation self-service analytics and AI-enabled decision support.

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At Vertex we embrace continuous learning and encourage employees to stay informed on advances in AI technology automation and industry trends. We seek talent who bring an external perspective demonstrate curiosity and sound judgment and thoughtfully adopt new capabilities that improve decision-making drive innovation increase efficiency and help deliver better outcomes for patients.

Pay Range:

Disclosure Statement:

Flex Designation:

Flex Eligibility Status:

In this Hybrid-Eligible role you can choose to be designated as:
1. Hybrid: work remotely up to two days per week; or select
2. On-Site: work five days per week on-site with ad hoc flexibility.

Note: The Flex status for this position is subject to Vertexs Policy on Flex @ Vertex Program and may be changed at any time.

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Company Information

Vertex is a global biotechnology company that invests in scientific innovation.

Vertex is committed to equal employment opportunity and non-discrimination for all employees and qualified applicants without regard to a persons race color sex gender identity or expression age religion national origin ancestry ethnicity disability veteran status genetic information sexual orientation marital status or any characteristic protected under applicable law. Vertex is an E-Verify Employer in the United States. Vertex will make reasonable accommodations for qualified individuals with known disabilities in accordance with applicable law.

Any applicant requiring an accommodation in connection with the hiring process and/or to perform the essential functions of the position for which the applicant has applied should make a request to the recruiter or hiring manager or contact Talent Acquisition at