Director, Data Intelligence

FranklinCovey

. Architect and lead FranklinCovey’s enterprise data strategy .

Posted 9/17/2026full-timeRemote • United StatesLead💰 $160,000 - $190,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in architecting and leading enterprise data strategies, including data infrastructure lifecycle management, data warehouse implementation, and machine learning applications. Proven ability to translate complex data into actionable business intelligence and establish governance frameworks that drive operational improvements.

Highest-signal resume keywords

Data Strategy LeadershipPython ProgrammingCloud Computing (AWS, Snowflake)Machine Learning ImplementationSQL Proficiency

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data EngineeringBusiness IntelligenceData Warehouse DesignETL Pipeline DevelopmentStatistical AnalysisKPI DevelopmentData GovernanceData Migration ManagementPredictive AnalyticsData Quality Improvement

Soft Skills

Cross-Functional LeadershipExcellent CommunicationSelf-StarterProblem-SolvingAdaptability

Tools & Technologies

JupyterScikit-learnXGBoostARIMAData Lakes

Industry Keywords

Professional ServicesSaaSSubscription-Based Business Models

Tech Stack

Tools & technologies

AWSCloudERPETLPythonScikit-LearnSQL

About the role

Key responsibilities & impact

Architect and lead FranklinCovey’s enterprise data strategy
Own the full data infrastructure lifecycle, including legacy-system migration and modern data warehouse implementation
Translate raw data into actionable business intelligence for Sales, Marketing, Finance, Operations, and Product
Establish KPI standards, governance frameworks, and a unified data operating model
Lead and mentor a Data & Analytics team of data engineers, analysts, and BI developers
Define team structure, hiring roadmap, and capability-development plan
Develop and execute a roadmap to unify data across company systems and functions
Create a company-wide KPI and metrics framework
Design, build, and maintain a reliable, scalable data warehouse
Manage third-party vendors and system integrators supporting migration
Ensure continuity for downstream reports and dashboards during platform migration
Partner with IT, Finance, and Operations to inventory source systems and document data lineage
Apply AI-assisted analytics and machine learning to generate predictive insights and automate workflows
Prototype solutions and shape an AI roadmap aligned with business needs
Make actionable recommendations and identify levers affecting target metrics
Establish data ownership, data contracts, and governance processes
Improve source data quality by resolving root-cause issues in ERP and operational systems
Enable consistent reporting, performance tracking, and future AI capabilities
Influence executive decision-making while reporting to the CFO and EVP Operations

Requirements

What you’ll need

Bachelor’s degree
7+ years of progressive experience in data engineering, analytics, or business intelligence
5+ years of experience writing Python code in a production environment
Experience with a cloud computing ecosystem, preferably AWS and Snowflake
Knowledge of machine learning models and statistical analysis
Experience using common ML frameworks, such as Jupyter, scikit-learn, XGBoost, and ARIMA
Expert-level SQL proficiency
Understanding of cloud architecture and data lakes
Proven experience delivering a data warehouse from design to production in a mid-to-large enterprise environment
Demonstrated experience managing a full-scale data migration, including legacy system decommissioning
Proven ability to translate data into measurable operational improvements beyond reporting and dashboards
Demonstrated experience applying machine learning and generative AI in a production environment
Ability to translate analytical outputs into clear business recommendations for non-technical audiences
Strong cross-functional leadership across operations, finance, commercial, and IT teams
Ability to operate effectively in fast-paced and ambiguous environments
Ability to develop ETL pipelines without the support of a full data engineering team
Experience in professional services, SaaS, or subscription-based business models
Excellent communication skills, including explaining machine-learning implications to non-technical stakeholders
Ability to work independently with little direction; self-starter

Benefits

Comp & perks

15% variable pay
Medical insurance
Dental insurance
Vision insurance
HSA
Employee stock purchasing program
401(k)
Paid time off
Holiday pay
Remote work
More benefits detailed at franklincoveybenefits.com