- Location
- 1168 RaceTrac Store Support Center, United States of America
- Type
- Full-time
- Department
- Management
- Seniority
- Director
- Experience
- 7+ years
- Source
- Workday
Description
RaceTrac Company Overview
Job Description:
Director, Data Strategy
Role Summary
The Director of Data Strategy shapes RaceTrac’s enterprise-wide vision for how data is governed, managed, activated, and measured. This leader ensures that data becomes a strategic asset—fueling revenue growth, operational efficiency, regulatory compliance, and exceptional guest experiences. Working across Technology, Analytics, Product, Finance, and Business Units, this role builds the roadmap, operating model, and culture needed to unlock the full value of data at scale.
Key Responsibilities
• Define a multi‑year enterprise data strategy aligned to business goals, including prioritization models and investment frameworks.
• Build and operationalize a scalable data operating model with clear ownership, standards, and funding structures.
• Lead enterprise data governance across privacy, security, lineage, quality, retention, and AI risk.
• Implement data stewardship, issue management, and measurable defect‑reduction processes.
• Oversee the lifecycle of high‑value data products and partner with engineering/analytics to define requirements and success metrics.
• Drive data literacy and self‑service adoption across business teams.
• Build business cases and track ROI for data initiatives; lead quarterly portfolio reviews with Finance and BU leaders.
• Align with Enterprise Data Architecture on platform strategy, including lakehouse/warehouse, MDM, catalog, governance tools, and AI/ML platforms.
• Promote standardized data models and interoperability across domains (finance, customer, product, operations, HR).
• Establish responsible AI guardrails and ensure data readiness for ML/GenAI use cases.
• Prioritize and incubate analytics and AI use cases with clear problem statements and measurable outcomes.
• Lead executive‑level communications, socialize strategy, and support change management across the enterprise.
• Build and operationalize a scalable data operating model with clear ownership, standards, and funding structures.
• Lead enterprise data governance across privacy, security, lineage, quality, retention, and AI risk.
• Implement data stewardship, issue management, and measurable defect‑reduction processes.
• Oversee the lifecycle of high‑value data products and partner with engineering/analytics to define requirements and success metrics.
• Drive data literacy and self‑service adoption across business teams.
• Build business cases and track ROI for data initiatives; lead quarterly portfolio reviews with Finance and BU leaders.
• Align with Enterprise Data Architecture on platform strategy, including lakehouse/warehouse, MDM, catalog, governance tools, and AI/ML platforms.
• Promote standardized data models and interoperability across domains (finance, customer, product, operations, HR).
• Establish responsible AI guardrails and ensure data readiness for ML/GenAI use cases.
• Prioritize and incubate analytics and AI use cases with clear problem statements and measurable outcomes.
• Lead executive‑level communications, socialize strategy, and support change management across the enterprise.
Success Measures (First 3–6 and 6–12 Months)
3–6 Months:
- Deliver an enterprise data strategy and operating model proposal.
- Stand up initial governance processes, including stewardship roles and issue‑management workflows.
- Establish baseline data quality, risk, and value‑tracking metrics.
- Build strong cross‑functional relationships with Technology, Finance, Product, and BU leaders.
6–12 Months:
- Launch prioritized data products with defined success metrics and adoption plans.
- Demonstrate measurable improvements in data quality, lineage visibility, and governance compliance.
- Implement value‑realization frameworks and complete the first portfolio review cycle.
- Advance AI/analytics readiness, including responsible AI guardrails and prioritized use‑case incubation.
Required Skills & Experience
• Bachelor’s or Master’s degree in Data Management, Computer Science, Information Systems, Business, or related field.
• 7+ years of experience in data management, governance, and analytics, including 5+ years in leadership roles.
• Strong understanding of data governance frameworks, data quality practices, and regulatory compliance (GDPR, CCPA).
• Experience with enterprise data platforms, cloud technologies, and data integration tools.
• Proven ability to influence senior stakeholders and drive cross‑functional alignment.
• Excellent communication and leadership skills with a track record of building high‑performing teams.
• Demonstrated ability to build and scale data & analytics organizations or practices.
• Experience implementing enterprise data governance processes (privacy, MDM, access rights, data integrity).
• Experience procuring and integrating third‑party data sources.
• 7+ years of experience in data management, governance, and analytics, including 5+ years in leadership roles.
• Strong understanding of data governance frameworks, data quality practices, and regulatory compliance (GDPR, CCPA).
• Experience with enterprise data platforms, cloud technologies, and data integration tools.
• Proven ability to influence senior stakeholders and drive cross‑functional alignment.
• Excellent communication and leadership skills with a track record of building high‑performing teams.
• Demonstrated ability to build and scale data & analytics organizations or practices.
• Experience implementing enterprise data governance processes (privacy, MDM, access rights, data integrity).
• Experience procuring and integrating third‑party data sources.
Preferred Skills
• Experience with BI and analytics tools such as Power BI, Tableau, or Looker.
• Familiarity with distributed data strategies and modern cloud storage technologies.
• Strong written and verbal communication skills with the ability to simplify complex concepts.
• Proven ability to build performance metrics for analytics models and digital properties.
• Familiarity with distributed data strategies and modern cloud storage technologies.
• Strong written and verbal communication skills with the ability to simplify complex concepts.
• Proven ability to build performance metrics for analytics models and digital properties.
Who You’ll Work With
• Reports to: Executive Director
• Works closely with: Technology, Analytics, Product, Finance, and Business Unit leadership teams
• Part of a team that values collaboration, communication, and doing what’s right for the guest
• Works closely with: Technology, Analytics, Product, Finance, and Business Unit leadership teams
• Part of a team that values collaboration, communication, and doing what’s right for the guest
Responsibilities:
Enterprise Data Strategy & Roadmap
- Define a multi-year data strategy aligned to enterprise goals; establish the portfolio, prioritization model, and investment thesis for data initiatives.
- Create a scalable data operating model (centralized/Hub-and-Spoke/Federated) with clear ownership (RACI), standards, and funding approach.
Data Governance & Risk
- Operationalize governance (policies, standards, controls) across privacy, security, lineage, quality, retention, and AI risk.
- Implement data stewardship and issue management with measurable defect reduction and remediation SLAs.
Data Products & Enablement
- Lead the definition and lifecycle of high-value data products.
- Partner with analytics/engineering to define product requirements, success metrics, and adoption plans.
- Drive data literacy programs and self-service adoption for business stakeholders.
Value Realization & Portfolio Management
- Build business cases and track ROI on data investments; establish value frameworks (revenue uplift, cost avoidance, risk reduction, cycle-time).
- Run quarterly portfolio reviews with Finance and BU leaders; rebalance based on outcomes.
Architecture Alignment & Platform Strategy
- Partner with Enterprise Data on platform roadmaps (data lakehouse/warehouse, MDM, catalog, governance tools, semantic layers, AI/ML platforms).
- Promote standardized data models and interoperability across domains (e.g., finance, customer, product, operations, HR).
AI/Advanced Analytics Readiness
- Establish responsible AI guardrails and data readiness for ML/GenAI use cases (access controls, PII handling, provenance, monitoring).
- Prioritize and incubate analytics/AI use cases with clear problem statements and success criteria.
Change Leadership & Communications
- Lead exec-level updates, socialize strategy, and align incentives with BU leaders.
- Develop communication plans for policy changes, new capabilities, and enablement milestones.
Qualifications:
All qualified applicants will receive consideration for employment with RaceTrac without regard to their race, national origin, religion, age, color, sex, sexual orientation, gender identity, disability, or protected veteran status, or any other characteristic protected by local, state, or federal laws, rules, or regulations.
Skills
TableauPower BILookerComplianceChange ManagementGDPR
