Principal Data Analyst, Enterprise Data Solutions

About the role

You will own the definition, delivery, and productization of Enterprise Data Solutions data assets from initial concept through production and customer feedback. You will translate commercial hypotheses and inbound demand into fully specified data products with clear schema, governance, and SLAs. This role blends product management discipline with hands-on analytical execution to build credible, repeatable, and exportable datasets. You will partner across Strategy, Engineering, Product, Data Science, Legal, and GTM to bring data products to market. You will lead design sessions, define customer-facing documentation, and establish readiness criteria for each new asset. You will also operate the customer feedback loop and drive continuous improvement based on pilot signals. This is a 0-to-1 builder role shaping the future of CarGurus Enterprise Data Solutions.

Key facts

What you'll do

• Own the end-to-end definition of EDS data products, starting with Search Trends, Price Trends, and Inventory Trends, and extending into the broader EDS portfolio (e.g., 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. Draw upon prior experience with expansive, unrefined datasets (e.g., user clickstream data) to fix modeling bottlenecks in quick, scalable, outside-of-the-box ways.
• Partner with Engineering to specify operational stability requirements, expected SLAs, support coverage, monitoring, and incident response, for assets being consumed externally.
• In partnership with the broader Data team, establish the standards and templates for how a CarGurus data asset becomes a saleable EDS product: readiness criteria, governance review, pricing/packaging input, and handoff to GTM.
• Define and operate the customer feedback loop, capturing signals from sales conversations and pilot customers and translating it into asset evolution, packaging changes, and roadmap inputs.
• In partnership with the broader Enterprise Data Solutions team, define and drive adoption of data quality and reliability standards for external data products.
• Design and implement testing and monitoring frameworks for data products to ensure ongoing integrity, accuracy, and performance in external consumption scenarios.
• Represent Enterprise Data Solutions in cross-functional roadmap discussions, providing analytical context, customer insights, and prioritization input.
• Collaborate closely with Data Engineering to optimize data pipelines, balance freshness and cost, and ensure robust data lineage and metadata management.
• Work with Sales and Customer Success to package offerings, define tiering, and support commercial conversations with data-literate stakeholders.
• Contribute to the development of internal tools and frameworks that enable faster iteration on data products and clearer ownership of data assets.

Requirements

• Bachelor's degree in a quantitative field or equivalent practical experience.
• Demonstrated experience owning data products end to end, including scoping, delivery, and operationalization.
• Strong proficiency in SQL and experience working with large, complex datasets in a data warehouse environment.
• Experience with modern data stack tools such as DBT and Snowflake, or equivalent platforms.
• Proven ability to translate business requirements into structured data specifications and documentation.
• Experience defining and maintaining data quality, SLAs, and monitoring for data products.
• Strong collaboration skills working with cross-functional stakeholders including Engineering, Product, and Sales.
• Comfort working in a fast-paced, ambiguous environment where priorities evolve quickly.
• Excellent written and verbal communication skills for both technical and non-technical audiences.
• Willingness to work closely with legal, compliance, and security teams to ensure data governance standards are met.
• Demonstrated ownership of end-to-end analytical solutions that are used by external customers or partners.
• Experience with pricing, packaging, or go-to-market handoffs for data products is strongly aligned with our needs.
• Background in the automotive marketplace or related industries is a plus but not required.

Practical notes

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