Director of Enterprise Intelligence

Yum!•Plano, TX

1d•$195,200 - $215,000

About The Position

The Director of Enterprise Intelligence owns and integrates digital analytics, experimentation, business intelligence, data engineering, and the Databricks Lakehouse into an enterprise decision platform for KFC US. The role is accountable for trusted restaurant, operational, financial, customer, and franchise reporting; certified metrics and semantic models; and productionizing advanced analytics and AI use cases, including recommended ordering, restaurant-level forecasts, cook projections, demand signals, promotion analysis, and customer intelligence. The position will evolve the platform to support governed self-service analytics and natural-language insight experiences through Databricks AI/BI and Genie AI, including secure access through Microsoft Teams where appropriate. The mandate is to establish consistent measurement and definitions, AI-ready production pipelines, responsible-AI controls, and useful insight experiences—dashboards, narratives, agents, and natural-language queries—that improve digital growth, restaurant performance, franchise confidence, and profitable decision-making. The role leads and develops a multidisciplinary team spanning digital analytics, BI, and data engineering; manages analytics, BI, and data platform vendors; and works across KFC US leadership, global data leadership, restaurant operations, finance, franchise operations, product, engineering, UX, marketing, and franchise stakeholders. The position supervises three exempt employees and no non-exempt employees.

Requirements

12+ years of experience in analytics, BI, data engineering, or applied decision science, with 6+ years leading enterprise teams and platforms.
Proven ownership of production BI, analytics, and data platforms supporting operational, financial, and franchise reporting at scale.
Strong experience with digital analytics and experimentation platforms such as Amplitude or equivalent, including metric trees and causal measurement.
Deep SQL expertise and strong understanding of data modeling, pipelines, analytics engineering, and semantic layers.
Demonstrated ability to establish and govern KPIs across digital, finance, operations, and franchise contexts.
Experience partnering with applied AI or data science teams to productionize models into business workflows.
Familiarity with LLM- and natural-language-based analytics patterns, including metric grounding, explainability, and guardrails over certified datasets.
Executive-, operator-, and franchise-level communication skills, with the ability to translate analytics and AI outputs into trusted, actionable decisions.
Hands-on knowledge of Databricks Lakehouse capabilities, including SQL, Unity Catalog, data and AI governance, semantic modeling, AI/BI dashboards, Databricks metrics, and Genie AI spaces or agents.
Demonstrated ability to ground natural-language analytics in certified data through attribution, metadata, metric definitions, model context, benchmarking, monitoring, permissions, and responsible-AI guardrails.
Experience leading proofs of concept from use-case definition and vendor alignment through validation, adoption, production readiness, and value measurement.
Strong customer and restaurant intelligence orientation, including the ability to connect digital behavior, transaction, operational, financial, and customer data to practical business decisions.

Nice To Haves

QSR, retail, or multi-unit franchise experience.
Experience integrating POS, kiosk, aggregator, loyalty, labor, and financial systems.
Exposure to forecasting, optimization, or recommendation systems, such as demand forecasting, recommended ordering, and labor or cook projections.
Experience enabling natural-language query or AI-assisted insight experiences over enterprise data.
Familiarity with modern BI, semantic modeling, and metrics-layer approaches.
Experience embedding governed Databricks Genie experiences in Microsoft Teams or comparable collaboration channels.
Familiarity with customer data platforms, tokenized customer data, Customer 360 modernization, and migration or decommissioning strategies.
Experience building analytics or AI guilds, training business users, and scaling self-service adoption across functions.
Commercial and delivery experience with Databricks and systems-integration partners, including sponsorship models, low-cost pilots, resourcing, and ROI evaluation.

Responsibilities

Own digital product analytics, session replay, and experimentation platforms, including their roadmaps, governance, adoption, and value realization.
Own Amplitude or an equivalent platform, 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 quality assurance, statistical guardrails, and results arbitration.
Operationalize digital experience signals such as rage clicks, errors, latency, and funnel drop-offs for engineering and product triage, with quantified business impact.
Own 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 with operational and financial close rhythms.
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 for 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.
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 data attribution, model and schema context, benchmarks, monitoring, permissions, and appropriate human review.
Establish boundaries and controls 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 governed access to Genie through collaboration channels such as Microsoft Teams, using query logs and user feedback to improve semantic models and adoption.
Manage Databricks and implementation partners through business cases, proofs of concept, capacity planning, commercial negotiation, delivery governance, and measurable value realization.
Serve as the single accountable owner for franchise-facing analytics and reporting experiences.
Ensure transparency, explainability, and consistency between corporate and franchise performance views.
Partner with Franchise Operations and Finance to evolve reporting based on operator needs and field feedback.
Lead and develop a multidisciplinary team spanning digital analytics, BI, and data engineering.
Establish communities of practice for analytics, experimentation, and data engineering standards.
Manage analytics, BI, and data platform vendors, including renewals, roadmaps, and return on investment.
Ensure compliance with data privacy, security, and regulatory standards, including CCPA/CPRA and GDPR where applicable.
Create cross-functional guilds and communities of practice to share Databricks and Genie AI learnings, accelerate reusable patterns, and raise data and AI fluency across KFC US.
Translate analytical findings into operational decisions, executive narratives, prioritized roadmaps, and accountable action plans.
Anticipate resource, data, integration, and vendor constraints; structure pragmatic proofs of concept and partner options to validate value before scaling.
Guide customer data platform strategy and modernization, including evaluation of emerging Databricks Customer Lake capabilities and migration of customer intelligence workloads into durable enterprise platforms.