We are the people who give possibilities purpose

BD is one of the largest global medical technology companies in the world. Advancing the world of health™ is our Purpose, and it’s no small feat. It takes the imagination and passion of all of us—from design and engineering to the manufacturing and marketing of our billions of MedTech products per year—to look at the impossible and find transformative solutions that turn dreams into possibilities.

Job Description

Enterprise Data Architecture and Strategy

• Define and execute the enterprise data strategy aligned with digital transformation, AI/ML enablement, and business priorities.

• Provide technical oversight for Databricks architecture and design standards, driving governance, performance optimization, and best practices across the data ecosystem.

• Drive standardization and simplification of data across ERP, CRM, supply chain, and analytics platforms.

• Define enterprise data models and interoperability standards across structured and unstructured data domains.

• Enable scalable, reusable, and governed data products to support advanced analytics and AI use cases.

Master Data Strategy, Architecture & Governance

• Define and lead the global Master Data Management (MDM) strategy across key domains: Product, Customer, Vendor/Supplier, Finance, and Location.

• Establish and drive clear business ownership of master data by identifying and formalizing domain-level data owners aligned to core business processes.

• Partner closely with Integrated Supply Chain, Global Commercial Operations, and Finance teams to embed master data ownership into end-to-end business process accountability.

• Define and standardize master data creation, maintenance, and governance processes aligned to enterprise process models (e.g., Quote to Cash, Source to Pay and Finance).

• Architect and govern MDM platforms and ecosystems, including deep expertise in:

  • Reltio (cloud-native, multi-domain MDM, event-driven integration)

  • SAP MDG (SAP aligned governance, workflow-driven master data control)

• Establish master data architectural patterns including registry, coexistence, consolidation, and centralized models.

• Define golden record strategy, survivorship rules, matching/merging algorithms, and hierarchy management.

• Implement field-level governance, including attribute-level ownership, validation rules, reference data alignment, and lifecycle controls.

• Drive modern MDM capabilities including real-time/event-driven MDM, API-first data services, graph-based relationships, and AI-assisted data stewardship.

• Ensure tight integration of MDM with upstream and downstream systems (ERP, CRM, supply chain platforms, and Databricks).

• Manage Product Information Management (PIM) team and define clear ownership for Product master data, ensuring alignment between Product master data, product content, and PIM processes.

• Partner with Regulatory and Quality teams to define standards and governance for PIM systems, ensuring that product attributes, labeling data, and regulatory-critical fields are accurate, controlled, and compliant across markets.

• Establish master data standards, policies, and controls to ensure consistency, quality, and compliance across all regions and business units.

• Incorporate modern enterprise MDM trends, including AI-assisted data matching and cleansing, smart suggestions for data stewards, and more automated workflows that reduce manual effort while improving master data quality.

Data Analytics Governance & Data Domain Ownership

• Own the enterprise data governance framework, including policies, standards, controls, and decision rights.

• Lead Databricks domain-based governance models with clear accountability for data owners, stewards, and custodians.

• Establish governance for data products, including certification, quality SLAs, lineage, and lifecycle management.

• Implement metadata-driven governance leveraging tools such as Collibra and/or Alation.

• Enforce data quality management practices including profiling, monitoring, issue remediation, and continuous improvement.

• Partner with Security and Privacy teams to embed data protection, access control (e.g., Immuta), and regulatory compliance.

Data Platforms Governance

• Provide strategic leadership and accountability for data platforms, data engineering, and analytics delivery, with a strong focus on Databricks lakehouse architecture.

• Oversee the governance, execution, and delivery of data engineering and analytics solutions, ensuring alignment with enterprise data strategy and governance standards.

• Lead the design and delivery of master data–driven data products across key domains, including Customer, Product, and Vendor/Supplier, enabling consistent, high-quality, and reusable datasets.

• Ensure master data is fully integrated into analytical and operational data pipelines, supporting both batch and real-time use cases.

• Establish standardized, scalable data models and pipelines that incorporate mastered data (golden records, hierarchies, reference data) into downstream analytics and AI use cases.

• Enable master data analytics capabilities, including domain-level insights (e.g., customer 360, product performance, supplier intelligence) driven by governed and trusted data.

• Partner with MDM platforms (Reltio, SAP MDG) to ensure seamless data synchronization with Databricks and other enterprise data platforms.

• Promote DataOps practices to ensure reliability, observability, and quality of data pipelines and data products.

• Drive adoption of data product thinking, ensuring each domain (Customer, Product, Vendor) has clearly defined ownership, SLAs, quality metrics, and lifecycle management.

Operating Model & Leadership

• Establish and lead a global Data & Master Data Center of Excellence (CoE).

• Define governance forums, escalation paths, and decision-making structures across regions and business units.

• Develop KPIs and OKRs to measure data quality, governance adoption, MDM effectiveness, and business value realization.

• Lead global teams, including internal staff, managed services, and system integrators.

• Act as a strategic advisor to executive leadership on data investments, risks, and transformation priorities.

Master Data Operations & Service Delivery

• Manage Master Data Operations organization within a Global Business Services (GBS) or shared services model.

• Define the operating model for centralized and/or federated master data creation, maintenance, and lifecycle management.

• Manage day-to-day master data activities including data creation, enrichment, validation, change management, and archival.

• Define and enforce service levels (SLAs/XLAs) for master data processing, turnaround times, and quality metrics.

• Implement workflow-driven data management processes integrated with platforms such as SAP MDG and Reltio.

• Ensure operational adherence to governance policies, data standards, and field-level controls.

• Drive continuous improvement through automation, AI-assisted data entry, and process optimization.

• Manage vendor and partner teams supporting master data operations.

What We’re Looking For

Required Qualifications

• Bachelor’s degree required; advanced degree preferred.

• 15+ years of experience in data strategy, data governance, MDM, analytics, or data engineering.

• Proven leadership in large, complex, global enterprises with multi-domain data environments.

• Deep expertise in Master Data Management, including hands-on knowledge of Reltio and/or SAP MDG.

• Strong understanding of master data architecture patterns, data modeling, and field-level governance.

• Experience with modern data platforms including Databricks (lakehouse architecture).

• Expertise in data governance tools such as Collibra, Alation, and data security platforms like Immuta.

• Experience building AI-ready data foundations and enabling analytics and machine learning at scale.

• Strong executive communication and stakeholder influence skills.

Preferred Experience

• Experience in regulated industries such as life sciences, healthcare, or manufacturing.

• Familiarity with data mesh, data fabric, and data product operating models.

• Experience managing global business services (GBS) or outsourced data operations.

• Exposure to AI-driven data management, including automated classification, quality, and stewardship.

• Track record of driving enterprise-wide adoption and change management.

Why Join Us?

To find purpose in the possibilities, we need people who can see the bigger picture, who understand the human story that underpins everything we do. We welcome people with the imagination and drive to help us reinvent the future of healthcare. At BD, you’ll discover a culture in which you can learn, grow and thrive.

We believe that when people connect in person, we learn faster, collaborate more deeply, and build a stronger culture. Join us and enjoy a culture where face-to-face collaboration supports your learning, your progress, and your success.

To learn more about BD visit https://bd.com/careers.

Becton, Dickinson, and Company is an Equal Opportunity Employer. We evaluate applicants without regard to race, color, religion, age, sex, creed, national origin, ancestry, citizenship status, marital or domestic or civil union status, familial status, affectional or sexual orientation, gender identity or expression, genetics, disability, military eligibility or veteran status, and other legally protected characteristics.

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Our salary or hourly rate ranges reward associates fairly and competitively. We regularly review these ranges and factors, such as location, contribute to the range displayed.

Our pay is based on the role and the necessary skills and education to perform it successfully. The salary or hourly rate offered is determined by the role's specific requirements, including any applicable step rate pay system at the work location. Salary or hourly pay ranges are influenced by labor laws and Collective Bargaining Agreement (CBA) requirements applicable to the work location which may also affect the workplace arrangement of the role.

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