Analytics Engineer

ABOUT CARTESIA

Our mission is to architect AI that learns from and interacts with the world like humans do.

We're pioneering the model architectures that will make this possible. Our founding team met as PhDs at the Stanford AI Lab, where we invented State Space Models or SSMs, a new primitive for training efficient, large-scale foundation models. Our team combines deep expertise in model innovation and systems engineering paired with a design-minded product engineering team to build and ship cutting edge models and experiences.

We're funded by leading investors at Index Ventures and Lightspeed Venture Partners, along with Factory, Conviction, A Star, General Catalyst, SV Angel, Databricks and others. We're fortunate to have the support of many amazing advisors, and 90+ angels across many industries, including the world's foremost experts in AI.

ABOUT THE ROLE

We're hiring an Analytics Engineer to build and own Cartesia's company-wide source of truth. You'll bring together product, billing, CRM, marketing, and operational data into reliable models and shared metric definitions that teams can trust.

This is an early, foundational data hire. You'll fix urgent correctness issues, strengthen the warehouse and transformation layer, and create the first trusted dashboards for Product, GTM, Growth, RevOps, and leadership. The goal is not simply to answer questions—it is to build the systems, models, and standards that let the company answer them consistently.

YOUR IMPACT

- Own the path from source systems to canonical datasets, metrics, and dashboards.

- Identify and fix data-quality issues across pipelines, models, definitions, and reporting surfaces.

- Build and maintain reliable warehouse models using SQL and dbt or equivalent tooling.

- Establish clear metric definitions, tests, lineage, freshness monitoring, documentation, and ownership.

- Partner closely with Product, Engineering, RevOps, Growth, and GTM to translate business concepts into durable data models.

- Create trusted dashboards for core company metrics such as activation, usage, billing, customer health, and marketing performance.

- Make common data questions self-serve while ensuring dashboards reuse canonical logic rather than duplicating it.

- Audit the existing data stack and recommend pragmatic improvements or overhauls to ETL and analytics tooling where needed.

- Educate the company on how to use the source of truth and how new metrics and dashboards should be created.

WHAT YOU BRING

- 5+ years in analytics engineering, data engineering, or a technically rigorous analytics role, ideally at a B2B SaaS or developer-tools company.

- Expert SQL and strong warehouse modeling fundamentals, including dimensional modeling, historization, and identity resolution.

- Production experience with dbt or similar transformation tooling, plus testing, orchestration, monitoring, lineage, and documentation.

- A track record of turning fragmented data and competing definitions into canonical, reusable models.

- Experience working across product, billing, CRM, and marketing data; self-serve funnel experience is especially valuable.

- Strong judgment about when a problem belongs in a source system, pipeline, warehouse model, semantic layer, or dashboard.

- The ability to investigate discrepancies end-to-end and prevent them from recurring—not just patch the final report.

- Strong stakeholder instincts and the ability to make ambiguous business concepts precise.

- A practical, low-ego approach: willing to fix urgent issues while building toward a durable foundation.

NICE-TO-HAVES

- Experience as an early or founding member of a data function.

- Experience with full-funnel growth analytics, attribution, channel ROI, CRM, billing, or self-serve conversion.

- Experience building self-serve data workflows or using LLM-powered analytics tooling.

NOT REQUIRED

- Machine learning, predictive modeling, or traditional data science experience …