Director of Enterprise Intelligence at Yum! Brands in Plano, TX

Job description

Purpose of The Job Own and integrate digital analytics, experimentation, business intelligence, data engineering, and the Databricks Lakehouse into a single enterprise-grade decision platform for KFC US. Evolve that platform to deliver governed self-service analytics and natural-language insight experiences through Databricks AI/BI and Genie AI, including secure Microsoft Teams access where appropriate. This role is accountable for trusted restaurant, operational, financial, customer, and franchise reporting; certified metrics and semantic models; and the productionization of advanced analytics and AI use cases such as recommended ordering, restaurant-level forecasts, cook projections, demand signals, promotion analysis, and customer intelligence. The mandate: one measurement backbone, consistent definitions, AI-ready production pipelines, responsible-AI controls, and insight experiences—dashboards, narratives, agents, and natural-language queries—that improve digital growth, restaurant performance, franchise confidence, and profitable decision-making. Job Responsibilities Digital Analytics, Experimentation & Experience Intelligence Own digital product analytics, session replay, and experimentation platforms end-to-end (roadmap, governance, adoption, and value realization). Accountable for Amplitude (or equivalent), including event taxonomy, identity resolution, metric trees, permissions, and budget. Lead the Experimentation Center of Excellence across digital, CRM, and on-platform experiences, including intake, prioritization, design QA, statistical guardrails, and results arbitration. Operationalize digital experience signals (rage clicks, errors, latency, funnel drop-offs) into engineering and product triage with quantified business impact. Enterprise BI & Reporting (Restaurant, Ops, Finance, Franchise) Own the end-to-end BI strategy for restaurant system, operational, financial, and franchise reporting across corporate and franchise audiences. Define and govern KPIs and scorecards for sales, transactions, digital mix, margin, labor, service, uptime, refunds, guest recovery, and marketplace performance. Ensure consistent metric definitions and reconciliations across digital, finance, operations, and franchise reporting. Deliver executive, finance, operator, and franchise-ready dashboards with clear narratives, comparability, and trust. Establish service levels for data freshness, accuracy, and availability aligned to operational and financial close rhythms. Data Engineering & Analytics Architecture Own analytics data engineering for digital, restaurant, and enterprise data domains, including ingestion, transformation, and semantic modeling. Partner with Enterprise Data, Finance, and Operations to design scalable pipelines from POS, kiosks, aggregators, loyalty, CRM, payments, and back-office systems. Establish source-of-truth tables, dimensional models, and certified datasets that power BI, analytics, experimentation, and AI use cases. Define data quality SLAs, automated validation, monitoring, and incident response for analytics pipelines. Ensure analytics architecture supports privacy-by-design, franchise segmentation, and secure data sharing. Databricks Lakehouse, AI/BI & Genie AI Own the Databricks analytics roadmap and operating model, including workspace strategy, Unity Catalog, certified datasets, semantic models, Databricks metrics, AI/BI dashboards, and Genie spaces. Design and govern Genie AI experiences that translate natural-language questions into trusted insights, with clear data attribution, model and schema context, benchmarks, monitoring, permissions, and appropriate human review. Establish boundaries and control sets for enterprise AI assistants, limiting access to approved domains and certified datasets while preventing unsupported causal claims. Lead pilots and scale-up plans for restaurant, digital sales, market-basket, customer, promotion, labor, waste, and operational intelligence use cases. Enable go