Principal Commercial Analytics Engineer - Business Decision Intelligence

Job Description Role Summary

The Principal Analytics Analyst is a thought leader and strategic driver for the commercial analytics semantic layer and BI user experience. This role influences the development and drives the execution of the analytics platform operating plan, establishing the enterprise-wide vision for how commercial data is modeled, governed, and consumed by business users. The Principal Analyst shapes semantic layer strategy, governs metric standards across Regions and Business Units, leads the most complex multi‑stakeholder BI programs, and serves as an expert voice in cross‑functional forums on data product strategy, analytics governance, and self‑service enablement at scale.

• Define and drive the enterprise semantic layer strategy for commercial 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 standards.
• Lead the evaluation, selection, and adoption of semantic layer and BI platform capabilities in partnership with IT, Data Engineering, and enterprise architecture teams.
• Serve as the authoritative voice on semantic layer design trade‑offs, resolving escalated conflicts between business metric definitions and physical data model constraints.

• Own the global self‑service analytics strategy, defining the operating model, tooling standards, and enablement programs that allow commercial users across all Regions and Business Units to access governed insights independently.
• Lead the design of modular, reusable analytics asset libraries that eliminate bespoke reporting and accelerate insight delivery across the commercial organization.
• Define self‑service maturity frameworks and adoption metrics, using data to prioritize investments and demonstrate the business value of analytics platform improvements.
• Champion data literacy across the commercial organization through executive‑level enablement programs, community of practice initiatives, and stakeholder education.

• Define UX/UI standards and design principles for the commercial analytics interface, ensuring that dashboards and reporting surfaces are intuitive, performant, and aligned to user needs at all levels of the organization.
• Lead the design of the most strategically critical BI dashboards and executive reporting surfaces, in close partnership with senior commercial leaders.
• Establish a governed component and template library for BI development across the analytics team, enabling consistent, high‑quality delivery at scale.
• Drive continuous improvement of the analytics user experience through structured feedback loops, usability research, and adoption analytics.

• Partner with Commercial Data Product Strategy to co‑own the enterprise business glossary, metric registry, and commercial KPI framework.
• Lead cross‑functional forums to align commercial stakeholders, data engineering, and governance teams on metric definitions, calculation methodologies, and data lineage.
• Resolve escalated metric conflicts and discrepancies across Regions, Business Units, and reporting platforms, establishing single sources of truth for key commercial KPIs.
• Ensure all semantic layer assets meet data governance, privacy, and access control requirements in partnership with enterprise data governance teams.

• 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…