Associate Director, Global Sales Analytics Engineering - Business Decision Intelligence

About this role



 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 Role Summary  

The Associate Director, Global Sales Analytics Engineering Lead is the   senior people   leader responsible for the strategy, delivery, and continuous evolution of the commercial analytics semantic layer and business intelligence engineering function. This role owns the enterprise vision for how commercial sales data is modeled, governed, and consumed by business users globally — and builds the team, operating model, and platform capabilities   required   to deliver governed, scalable, and trusted analytics at scal e.  

A defining priority for this role is leading the transformation of the commercial analytics function from reactive, dashboard-centric reporting to a proactive, AI-ready analytics model — where governed data products, predictive signals, and intelligent alerting replace static reports as the primary vehicle for commercial insight delivery. This leader will champion the architectural and cultural shift   required   to move the organization from answering yesterday's questions to   anticipating   tomorrow's decisions.  

As a member of the commercial analytics leadership team, this role   partners directly with peers across Commercial Data Product Strategy, Decision Science & AI, Data Engineering, and IT to shape the enterprise data and analytics platform roadmap. The Associate Director leads a team of JG3–JG5 analysts and senior individual contributors, setting performance expectations, developing talent, and creating a culture of technical craftsmanship, stakeholder focus, and continuous improvement.  

Key Responsibilities  

Team Leadership & Organizational Development  

• Lead, manage, and develop a high-performing team   of Analytics   Analysts (JG3–JG5) and senior individual contributors across semantic layer engineering and BI development disciplines.  

• Set clear performance expectations, provide ongoing coaching and feedback, and conduct formal performance and development reviews for all direct reports.  

• Own team hiring, onboarding, and workforce planning in partnership with HR and analytics leadership, ensuring the team has the skills and capacity to deliver on the roadmap.  

• Build a   team   culture of technical excellence, accountability, stakeholder orientation, and continuous improvement.  

• Represent the analytics engineering function on the commercial analytics leadership team, contributing to cross-functional strategy, prioritization, and organizational decisions.  

Enterprise Semantic Layer Strategy & Governance  

• Define and own the enterprise semantic layer strategy for commercial sales analytics —   establishing   the long-term vision for how business metrics, dimensions, and hierarchies are governed and consumed globally.  

• Set the direction for semantic layer architecture across tooling platforms, ensuring scalability, consistency, and alignment to enterprise data governance and access control standards.  

• Serve as the ultimate authority on semantic layer design trade-offs, resolving escalated conflicts between business metric definitions and physical data model constraints.  

• Partner with Commercial Data Product Strategy to co-own the enterprise business glossary, metric registry, and commercial KPI framework.  

• Lead cross-functional governance forums to align commercial stakeholders, data engineering, and enterprise architecture teams on metric definitions, calculation methodologies, and data lineage.  

Global Self-Service Analytics Operating Model  

• Own the global self-service analytics operating model — defining the standards, tooling, enablement programs, and governance processes that allow commercial users across all Regions and Business Units to access governed insights independently.  

• Define self-service maturity frameworks,   adoption   KPIs, and investment priorities, using data to   demonstrate   business value and guide platform decisions.  

• Champion data literacy across the commercial organization through executive-level enablement programs,   community of   practice initiatives, and structured user education.  

• Drive the elimination of bespoke reporting by leading the design of modular, reusable analytics asset libraries that accelerate insight delivery at scale.  

Analytics Platform & BI Engineering Excellence  

• Lead the evaluation,   selection , and adoption of semantic layer, BI, and analytics platform capabilities in partnership with IT, Data Engineering, and enterprise architecture teams.  

• Establish and enforce BI development standards, design systems,   component   libraries, and UX/UI principles that ensure consistent, high-quality analytics experiences across the commercial organization.  

• Ensure all semantic layer assets and BI deliverables meet data governance, privacy, access control, and regulatory compliance requirements.  

• Drive continuous improvement of platform performance, semantic layer health, and report reliability through structured operational reviews and engineering best practices.  

  Agentic AI Development & Training • Define and drive the enterprise strategy for agentic AI integration within the commercial analytics platform, establishing the vision for how AI agents consume, generate, and augment governed semantic layer assets. • Lead the architecture and governance of AI training data programs, ensuring commercial analytics outputs used for model development meet quality, lineage, and compliance standards. • Partner with Decision Science & A

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