Senior Product Data Analyst

About the role

At CarGurus (NASDAQ: CARG), our mission is to give people the power to reach their destination. We started as a small team of developers determined to bring trust and transparency to car shopping. Since then, our history of innovation and go-to-market acceleration has driven industry-leading growth. In fact, we're the largest and fastest-growing automotive marketplace, and we've been profitable for over 15 years. What we do The market is evolving, and we are too, moving the entire automotive journey online and guiding our customers through every step. That includes everything from the sale of an old car to the financing, purchase, and delivery of a new one. Today, tens of millions of consumers visit CarGurus.com each month, and ~30,000 dealerships use our products. But they're not the only ones who love CarGurus-our employees do, too. We have a people-first culture that fosters kindness, collaboration, and innovation, and empowers our Gurus with tools to fuel their career growth. Disrupting a trillion-dollar industry requires fresh and diverse perspectives. Come join us for the ride! Role overview CarGurus is looking for a Senior Product Data Analyst to navigate our rich data environment and drive impactful outcomes for our customers and business. You will work cross-functionally to build out core data assets, empower go-to-market teams with revenue-generating insights, and design novel metrics to guide the business. Our Product Data Analytics org supports our Product, Engineering, and Go-to-Market teams, providing the final word on all analytics for CarGurus' user and dealer experiences. We are looking for thoughtful, curious, internally driven analytics experts who don't just pull data, but draw the "so-what" from complex, disparate sources and present these findings to internal teams and external customers.

Responsibilities

• Communicate and present complex quantitative findings in easily digestible terms to company leadership, homing in on key takeaways. Concretely and informatively respond to any probing, on-the-spot follow-up questions from senior decision-makers.
• Architect new data assets and production-grade data marts via dbt. You will fix modeling bottlenecks and optimize data lineages to ensure scalability.
• Serve as the technical expert in high-stakes Go-To-Market engagements. You will partner with Sales and Customer Success teams to provide data-backed proof of value, participating in client-facing meetings to help secure new partnerships or mitigate churn through sophisticated impact analysis.
• Partner with engineering teams to advance the company's core data modeling and architecture, with a focus on optimizing, integrating, and distilling large raw datasets and metadata.
• Use Claude Code and agentic workflows to automate, to automate, accelerate, and escalate the sophistication of your analyses.
• Advocate for specific, data-driven product innovations that help further company strategy, primarily in partnership with the Product, Engineering, and Go-to-Market teams.
• Lead rigorous, holistic discussions about statistical analyses and ML modeling. You are expected to be a "self-teaching" practitioner, staying ahead of quantitative trends.
• Participate in brainstorming and planning discussions across the organization to these ends. Avoid passivity in the face of flawed proposals; tactfully and persuasively push back against potential missteps.
• Craft the metrics that define success. You'll condense abstract or loosely-defined concepts into concrete calculations and relentlessly audit existing metrics for accuracy and relevance.
• Build intuitive dashboards and other visual monitoring tools to guide stakeholders' daily decision-making. Experiment with new kinds of visualizations that you believe could be better utilized in the organization. Re-work underlying code to appropriately structure visualization inputs.

Requirements

• 4+ years of experience in analytics, with a proven track record of influencing business strategy through complex modeling and applied statistics.
• Expert-level skills in SQL, Python, and dbt. Proficiency with file manipulation and version control via Git.
• Strong data storytelling and communication skills, presenting fin

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