Principal Data Analyst, Enterprise Data Solutions

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.

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 building Enterprise Data Solutions (EDS), a new customer facing solution that packages data and analytics based on CarGurus core assets.

Our objective is to leverage our differentiated marketplace data (Search Trends, Price Trends, Inventory Trends, and a growing portfolio of derived signals) to develop governed, exportable datasets for OEMs, lenders, insurers, investors, agencies, and consultants. This role spans the full lifecycle of an Enterprise Data asset, from creation through productization. You will both build the underlying data assets (modeling, aggregation, quality) and shape them into externally consumable products (schema, delivery, documentation, SLAs, customer feedback).

This is a 0-to-1 builder role. The successful candidate will translate an inbound demand signal or commercial hypothesis into a production-ready data product, defining the schema, delivery mechanism, documentation, SLAs, and feedback loop, and partner with Strategy, Engineering, Product, Data Science, Legal, and GTM to bring it to market.

What You'Ll Do

, Market Days Supply, Demand Relative to Supply, Estimated Retail Sales). Translate inbound customer signals and commercial hypotheses into clearly scoped data products: row/column structure, granularity, historical depth, refresh cadence, aggregation standards, and governance posture. Productionalize a controlled set of exportable datasets that are repeatable, governed, and exportable in a manner that meets external SLAs and data-quality standards.

Lead product-level design sessions to define MVP scope, delivery approach (Snowflake share, SFTP, API), and required engineering investment. Define and document customer-facing artifacts: data dictionaries, sample assets, schema documentation, and use-case framing for each asset. Conceive of new data assets and prototype them via automated transformations (primarily using DBT).

Partner with Data Engineering teams to optimize, integrate, and distill raw logs and metadata, advancing the company’s core data architecture and modeling . , user clickstream data) to fix modeling bottlenecks in quick, scalable, outside-of-the-box ways.

Requirements

, expected SLAs, support coverage, monitoring, and incident response, for assets being consumed externally.