Associate Director, Global Sales Analytics Engineering - Business Decision Intelligence

Role Summary

The Associate Director, Global Sales Analytics Engineering Lead is thesenior peopleleader 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 capabilitiesrequiredto deliver governed, scalable, and trusted analytics at scale.

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 shiftrequiredto move the organization from answering yesterday's questions toanticipatingtomorrow's decisions.

As a member of the commercial analytics leadership team, this rolepartners 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 teamof AnalyticsAnalysts (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 ateamculture 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 —establishingthe 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,adoptionKPIs, and investment priorities, using data todemonstratebusiness value and guide platform decisions.
• Champion data literacy across the commercial organization through executive-level enablement programs,community ofpractice 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,componentlibraries, 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 & AI and enterprise architecture teams to define reference architectures for agentic AI workflows embedded in commercial reporting, forecasting, and decision intelligence use cases.
• Establish enterprise-wide standards for prompt engineering, AI output validation, and human-in-the-loop governance across commercial analytics agentic workflows.
• Represent the analytics function in senior cross-functional forums on AI platform strategy, contributing to investment decisions, risk governance, and responsible AI frameworks.
• Mentor JG3 and JG4 analysts in agentic AI development and training practices, fostering a culture of responsible, governed AI adoption within the commercial analytics team.
• Track and assess the maturity of agentic AI capabilities across the commercial analytics ecosystem, defining roadmap priorities and success metrics that align to enterprise AI strategy.

Transformation from Reactive Reporting to Proactive, AI-Ready Data Products

• Own and drive the strategic transformation of commercial analytics from reactive dashboard reporting to proactive, AI-ready data products —establishingthe vision, roadmap, and delivery model for this multi-year capability shift.
• Redesign the analytics asset portfolio to prioritize intelligent, event-driven data products that surface insights, anomalies, and recommendations proactively — reducing reliance on manually-queried dashboards and static reports.
• Partner with Decision Science & AI teams to architect semantic layer and data product foundations that are consumption-ready for machine learning models, predictive analytics, and AI-generated insights.
• Define and enforce data product standards — including freshness SLAs, semantic consistency, access controls, and lineage documentation — that enable safe, scalable AI and analytics consumption across the commercial organization.
• Lead the commercial organization through the cultural and behavioral changerequiredto shift from pull-based report consumption to proactive, insight-driven workflows, working closely with business leaders to drive adoption.
• Establish metrics to track the function's progress on the reactive-to-proactive transformation, including reductions in ad hoc report requests, increases in proactive alert adoption, and AI model consumption of governed data products.

Executive Stakeholder Engagement & Strategic Influence

• Build andmaintaintrusted relationships with senior commercial leaders across Regions and Business Units, acting as a strategic analytics advisor and translating complex business needs into platform and capability investments.
• Represent the analytics engineering function in enterprise data strategy forums, contributing to platform investment decisions, vendor evaluations, and governance policy.
• Partner with the Decision Science & AI team to ensure semantic layer assets support AI model explainability, output visualization, and executive decision dashboards.
• Communicate team roadmap, delivery progress, and platform performance to analytics leadership and senior commercial stakeholders on a regularcadence.

Qualifications

• Bachelor's degree in BusinessAnalytics, Information Systems, Computer Science, Data Science, ora relatedfield required. Advanceddegreestrongly preferred.
• Demonstrated experience leading or contributing to a transformation from reactive BI/reporting to proactive, AI-ready data products — including data product design, semantic layer modernization, or analytics platform re-architecture.
• 8+ years of progressive experience in analytics, business intelligence, or data engineering, withdemonstratedincreasing scope and seniority.
• 3+ years in a formal people leadership role, with experience managing analytics or data professionals across multiple levels (e.g., analysts, senior analysts, principal/staff ICs).
• Deepexpertisein enterprise semantic layer design, governance, and platform strategy across one or more platforms (e.g.,LookML/Looker,AtScale,dbtMetrics, Power BI Semantic Models, Microsoft Fabric).
• Proventrack recordowning and delivering enterprise-scale self-service analytics programs across multiple Business Units or Regions.
• Advanced SQL skills and strong working knowledge of modern cloud data platforms (e.g., Snowflake,BigQuery, Databricks).
• Demonstrated experience as a thought leader in analytics — shaping platform strategy, metric governance frameworks, or BI architecture at an enterprise level.
• Deep understanding of commercial sales data domains: revenue, pipeline/opportunity, forecasting, pricing/CPQ, territory/account planning, and commercial KPIs.
• Strong executive communication and influence skills, with experiencepresentingtoand advising senior business and commercial leaders.
• Proven ability tooperatein a matrixed, global organization — building alignment across data engineering, governance, finance, and commercial stakeholder communities.

Desired Skills & Experience

• Hands-on experience designing or consuming AI-ready data products, including familiarity with feature stores, ML pipeline data contracts, or model output visualization.
• Experience defining and implementing enterprise metric frameworks, KPI registries, or commercial data glossaries at scale.
• Hands-on experience across multiple semantic layer platforms, with the ability to advise on tool selection, migration strategy, and vendor partnerships.
• Familiarity with AI/ML output visualization, model explainability dashboards, and decision intelligence use cases.
• Experience in MedTech, Life Sciences, or similarly regulated and commercially complex environments.
• Global experience leading analytics programs that serve multiple Regions and Business Units with shared, governed assets.
• Experience building or scaling an analytics engineering practice — including hiring, capability development, and operating model design.