Principal Data Analyst, Enterprise Data Solutions

CarGurus•Boston, MA

5d•Hybrid

About The Position

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). It blends product management discipline with hands-on data analytics ownership, ensuring every asset we deliver is credible, governed, repeatable, and customer-ready from day one. 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.

Requirements

6+ years of experience in Data Analytics, Data Product Management, Analytics Engineering, or a hybrid role combining data architecture with product ownership.
Demonstrated experience taking a data asset from concept to externally consumable product, including schema design, documentation, governance, and delivery via at least one of: Snowflake data share, SFTP, or API.
Strong product instincts: ability to define scope, make tradeoffs, set readiness criteria, and own a roadmap, not just execute against requirements.
Deep SQL fluency and working knowledge of modern data stack tooling (dbt, Snowflake, Looker/Omni, Snowplow or equivalent event infrastructure).
Track record of operating in a cross-functional environment, partnering with Engineering, Strategy, Legal, and GTM stakeholders, and driving alignment across senior leaders.
Comfort with ambiguity; ability to set direction in a 0-to-1 environment where standards and templates do not yet exist.
A natural thought leader with a proven track record of influencing business strategy
Experience leveraging Gen AI to optimize workflows, create tooling, and otherwise enhance your ability to execute
Deep understanding of and experience using analytical concepts and statistical techniques in the following areas: hypothesis testing, building advanced python scripts to automate rigorous statistical analyses, invoking/understanding parametric and nonparametric regression techniques, and more
Experience engaging directly with end customers or customer-facing teams to gather real-world product feedback, translate user needs into analytical requirements, and refine data assets based on how they are actually consumed

Nice To Haves

Experience launching or scaling a commercial data product, data marketplace listing, or external data-sharing relationship is preferred.
Familiarity with Snowflake Marketplace, data clean rooms, and external data delivery patterns is preferred.
Exposure to automotive, marketplace, or B2B SaaS data domains is preferred.
Experience defining SLAs, monitoring frameworks, and operational support models for externally consumed data is preferred.
Preferred tools/programs: Snowflake, Snowplow, DBT, Python, Looker/LookML, Jira, Google/MS suite, Omni.

Responsibilities

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 Data team, build a productization framework that can be extended across the broader monetization portfolio so that learnings, standards, and shared assets are reused, not duplicated.