Vp Jobs

About The Position

Requirements

• 15+ years of progressive data leadership experience, with 5+ years at the VP/SVP level leading multidisciplinary organizations across data engineering, analytics, and data science
• Demonstrated history influencing C-suite leaders and public company Boards to secure investment and deliver enterprise-scale data transformation initiatives
• Strong foundational experience in database architecture, data engineering, or data platform design, with the ability to deeply understand data pipelines, schema design, and system-level data integrity
• Demonstrated ability to steer and evaluate enterprise data platform decisions across storage, compute, orchestration, and semantic modeling, prioritizing long-term architectural integrity over tool familiarity
• Proven track record guiding teams toward trusted, high-integrity data pipelines and “single source of truth” environments
• Experience selecting and evolving data stack components based on business needs and scalability requirements, without being dependent on any single vendor ecosystem
• Comfort engaging in detailed architectural reviews while maintaining executive-level perspective
• Working understanding of the systems, data infrastructure, and experimentation frameworks required to enable scalable AI/ML initiatives
• Experience supporting rapid model experimentation and productionization environments, without needing to serve as a hands-on modeler
• Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related technical discipline, or equivalent practical experience

Nice To Haves

• Advanced degree (e.g., MS, MBA) is a plus but not required

Responsibilities

• Establish a trusted single source of truth for core enterprise metrics
• Define and execute a multi-year enterprise data roadmap spanning engineering, analytics, and science
• Align data capabilities to support marketplace growth, revenue expansion, and operational excellence
• Accountable for defining and enforcing enterprise-wide standards for data definitions, metric integrity, and stewardship across all business functions
• Own the identification and mitigation of enterprise data risks, including metric inconsistency, governance gaps, architectural fragility, and compliance exposure
• Establish clarity across departments on trusted performance indicators
• Execute a multi-year modernization plan to democratize data consumption across the company with a high degree of confidence behind the metrics
• Deliver meaningful business utility within the first 6 months while driving long-term architectural integrity
• Rationalize a highly fragmented legacy data schema into scalable, domain-oriented models with clear sources of truth
• Establish instrumentation and data contracts that enforce trust, and support near real-time experimentation and optimization
• Establish engineering excellence standards that improve data reliability observability, and deployment rigor across the platform.
• Establish architectural foundations capable of supporting event-driven intelligence and AI readiness
• Drive the evolution from static reporting to near real-time, decision-ready intelligence across marketplace performance, revenue operations, and executive KPIs
• Transition data from a reporting function to a revenue-driving capability by building trust around metrics and quality drivers that ensure accuracy
• Improve time-to-insight for revenue, dealership performance, and marketplace KPIs
• Enable reliable forecasting, pricing optimization, and experimentation frameworks
• Advise the CEO and Executive Leadership Team on data-related capital allocation, technology investments, and risk tradeoffs, ensuring disciplined ROI and long-term platform sustainability
• Enable a “culture of acceleration” across the Cars Commerce business that increases velocity and impact over time through democratized, trusted data
• Integrate and align multidisciplinary data functions under a unified operating model
• Build an “accelerating organization” that increases delivery velocity and technical rigor over time
• Serve as executive sponsor for data-related investment strategy, vendor partnerships, and cloud spend optimization
• Recruit, develop, and retain top-tier data leadership talent
• Oversee applied data science initiatives including predictive modeling, optimization, and experimentation
• Ensure models integrate directly into product and revenue workflows
• Balance long-term AI readiness with practical business impact
• Establish pragmatic data governance and stewardship frameworks, working across the enterprise to ensure coordination with appropriate business, legal, information security and finance partners
• Ensure data accuracy, availability, security, and compliance
• Drive clarity on metric ownership and accountability across the enterprise

Benefits

• Medical, Dental & Vision Healthcare Plans
• New Hire Stipend for Home Office Set-Up
• Generous PTO
• Paid Holidays, Floating Holiday, Volunteer Day, Recharge Day