Manager, Product Data Analytics

Who we are
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!

Product Data Analytics Managers lead a team of highly skilled product data analyst that support our Product and Engineering teams, providing the final word on all analytics for CarGurus’ user and dealer experiences. The ideal candidate has a knack for seeing solutions in sprawling data sets and the business mindset to convert insights into strategic opportunities for our company.

You’ll work closely with leaders across engineering, product, data engineering, data science, marketing, and other go-to-market teams to support and implement high-quality, data-driven decisions. In doing so, you will gain exposure to multiple dimensions of the business and cross-functionally influence many different roadmaps in addition to your own.

What You’ll Tackle:

• Exploration: Using your SQL/Python expertise, lead a team of analysts to conduct exploratory empirical analyses that bridge disparate data sources (e.g. clickstream data, subscription records, inventory volumes, etc.) to quantify product performance, user behavior, and/or market trends. Relentlessly dig into the data – consulting with other individuals and teams as you judge necessary.
• Process: Design and build technical processes to address business issues. Oversee the data/report requests process: tracking requests submitted, prioritization, approval, etc. Manage and optimize processes for data intake, validation, mining and engineering as well as modeling, visualization and communication deliverables.
• Metrics: Oversee the design and delivery of reports and insights that analyze business functions and key operations and performance metrics. Craft the metrics that define business success, condensing abstract or loosely-defined concepts down to concrete calculations. Audit and improve existing metrics to better inform the business’ needs.
• Advocacy: Advocate for specific, data-driven product innovations that help further high-level company strategy, primarily in partnership with the Product/Engineering teams. 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.
• Experimentation: Be a source of guidance for A/B experimentation, advising engineers, product managers, and high-level stakeholders on everything from the necessary data points to collect, required sample sizes, optimal metrics to examine, robustness of numerical findings, and the bottom-line success or failure of the tested changes. Suggest improvements to A/B testing-related tools, algorithms, and automated processes.
• Visualizations: Build intuitive dashboards and other visual monitoring tools to guide daily decision-making by senior stakeholders and the company at large. 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.
• Architecture: Conceive of new data assets and build automated transformations (via DBT, LookML, etc.) to bring them to fruition. Partner with Data Engineering teams to advance core data modeling/architecture (e.g. user clickstream logs), by optimizing, integrating, and distilling large raw datasets and metadata. Draw upon prior experience with expansive, unrefined datasets to fix modeling bottlenecks in quick, scalable, outside-of-the-box ways.
• Communication: Communicate and present complex quantitative findings in easily dig