VP, Head of Data

Cars Commerce

1d•Remote

About The Position

At Cars Commerce, we are dedicated to simplifying car buying and selling through innovative technology and a collaborative culture. The company operates several platforms including Cars.com, Dealer Inspire, AccuTrade, Dealerclub, and the Cars Commerce Media Network, aiming to enhance, measure, and drive local automotive retail. Cars Commerce emphasizes values such as collaboration, openness, challenging the status quo, taking ownership, and doing the right thing. Recognized as a U.S. News & World Report Best Company to Work For in 2024, the company prioritizes employee well-being, including pay, benefits, and work-life balance. The Head of Data role is a strategic and execution-oriented position responsible for leading the enterprise data organization through modernization and integration. Reporting to the CTO and collaborating with the CEO and Executive Leadership Team, this leader will unify Data Engineering, Data Science, Revenue Analytics, and Business Intelligence. The primary goal is to drive architectural stabilization, governance clarity, and improved decision velocity across the company, transforming a fragmented data environment into a cohesive, high-performing organization that delivers measurable business value. This role is crucial for shaping the enterprise data foundation of a scaled digital marketplace, influencing how performance is measured, governed, and translated into strategic action, and leveraging data as a strategic asset.

Requirements

Seasoned data executive who operates as both strategist and practitioner
Led multidisciplinary data organizations at scale and understand how to modernize legacy environments without disrupting business continuity
Treat data as a business function, not just a technical one
Bring the mindset of an engineer, grounded in systems thinking, architectural integrity, and disciplined execution, even as you operate at the highest levels of enterprise leadership
15+ years of progressive data leadership experience
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