Director, Analytics Data Products & Architecture

Director, Analytics Data Products & Architecture

The Director of Analytics Data Products & Architecture is a strategic and executional leader within RaceTrac’s Enterprise Strategic Analytics & Data Science (ESADS) team. This role exists to transform analytics and data science outputs into scalable, trusted data products that accelerate decision-making and AI adoption across the enterprise. Reporting to the Head of ESADS, the Director partners with IT, Strategic Analytics Partners (SAPs), and functional leaders to ensure the right data structures, curated logic, and reusable products are in place to power dashboards, models, and automated decisions. This is a builder and partner role—shaping the data product vision, embedding governance, and ensuring every data product earns business trust and delivers measurable value.

What You7ll Do

Scope of Role:

• Lead the Analytics Data Product Lifecycle: Own how data is structured, defined, documented, and maintained to support analytics and data science across the company.
• Turn Business Questions into Data Products: Partner with analytics teams and stakeholders to understand their needs, design reusable datasets, and ensure definitions are clear and agreed upon.
• Ensure Quality and Trust: Establish and enforce standards for documentation, data validation, and change management so analytics data is consistent and reliable. Includes partnering with IT on Data Governance, Data Quality, etc.
• Manage the Roadmap: Maintain a living, prioritized view of analytics data products being developed, improved, or retired, ensuring the focus stays on what brings the most value to the business.
• Simplify and Standardize: Identify redundant or conflicting datasets and drive efforts to consolidate them into certified, widely-used sources of truth.
• Be a Hands-On Leader: Step in directly to review data transformations, validate logic, troubleshoot issues, and guide builds to completion.
• Enable Faster Analytics: Work with analytics teams to ensure every data product removes friction, speeds up delivery of insights, and avoids rework across projects.

Day-to-Day Activities

• Meet regularly with analytics partners to capture new data needs and ensure existing data products still meet business expectations.
• Review logic and transformations in analytics data products to confirm they align with agreed business rules and calculations.
• Maintain a central inventory of analytics data products with clear purpose, owners, definitions, quality status, and adoption metrics.
• Facilitate working sessions with stakeholders to resolve data definition disputes and align on key KPIs or calculation methods.
• Test and validate data products hands-on before release, checking for accuracy, completeness, and usability.
• Track changes and improvements to data products, making sure updates are communicated, documented, and properly versioned.
• Lead efforts to retire outdated or low-quality data sets, migrating users to standardized, certified sources.
• Act as a connector between data producers and analytics users, making sure the data is fit for purpose and available when needed.

Expected Outcomes

• Analytics data products are defined, owned, and trusted by the analytics community and business users.
• Reduced duplication of datasets and logic, improving efficiency and consistency in reporting and modeling.
• Clear and agreed-upon definitions and KPIs across analytics and business teams.
• Faster analytics delivery, as reusable data products cut down on repeated data preparation work.
• Higher confidence in decisions, as data feeding reports, dashboards, and models is consistent and validated.
• ESADS is recognized as a trusted partner for making data usable, reliable, and ready for analytics and AI initiatives.

Complexities

• Ambiguity of Business Needs: Analytics questions often start out vague or inconsistent across teams. This role must clarify requirements, align definitions, and translate them into data products that can be used consistently across the enterprise.
• Multiple Stakeholders: Data products touch many teams—analytics, reporting, operations, finance, marketing, and others. Each has different needs and perspectives, requiring strong facilitation and alignment skills.
• Changing Priorities: Business needs, priorities, and available data evolve quickly. The role must balance stability (certified sources of truth) with flexibility to adjust and expand data products as the business changes.
• Complex Data Landscape: RaceTrac has many systems, legacy tables, and multiple environments. The Director needs to navigate this complexity to deliver simplified, usable analytics data without creating new silos.
• Quality and Trust: Data products must be accurate and reliable. This role leads the charge on testing, documentation, and change control to ensure confidence in analytics data.
• Influence Without Full Control: Many parts of the data value chain sit in other teams. This role succeeds by driving alignment, solving conflicts, and influencing outcomes without having direct ownership of all data sources.
• Balancing Speed and Rigor: The business needs fast insights, but certified data products require careful design, testing, and governance. This role manages that tension, ensuring speed never compromises trust.

Required Experience

What We7re Looking For:

• 10+ years in data, analytics, or business intelligence roles, with strong experience in data modeling, governance, and stakeholder management.
• Demonstrated success creating or managing curated data layers, metrics stores, or enterprise reporting logic.
• Experience partnering across both IT and business to develop shared logic and standards.
• Strong SQL and data transformation skills with exposure to cloud platforms (Azure, Databricks, Palantir, PowerBI).
• Ability to navigate ambiguity and influence cross-functional stakeholders to drive alignment and adoption.

Beneficial Experience

• Experience in retail, fuel, convenience, or logistics industries with awareness of how data drives decision-making.
• Familiarity with data governance tools and frameworks (e.g., Unity Catalog, Collibra, Alation).
• Experience building Data Quality capabilities and injecting into the data process (e.g. BigEye, Qualytics, etc.)
• Previous experience building a data product function or serving as a data steward.
• Bachelor's or Master 0degree in Business, Analytics, MIS, or a related field.

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