Director of Data Strategy

RaceTrac•Atlanta, GA

About The Position

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.

Requirements

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.

Nice To Haves

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.

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.
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.
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.
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.
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.
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).
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.
Lead exec-level updates, socialize strategy, and align incentives with BU leaders. Develop communication plans for policy changes, new capabilities, and enablement milestones.