Is the Analytics Engineer role remote?

No — this is an on-site role in San Francisco, CA, United States.

How much experience is required?

At least 5 years of relevant experience for this Analytics Engineer role.

Where is the role based?

Cartesia is hiring for this position in San Francisco, CA, United States.

What's the tech stack?

Joblaze extracted these technologies from the posting: SQL, dbt.

Does Cartesia sponsor work visas for this role?

Yes — the posting indicates visa sponsorship is available for the right candidate.

What seniority level is this role?

Cartesia targets mid-level candidates for this position.

Is this full-time or contract?

Full-time for this Analytics Engineer role at Cartesia.

Joblaze summary

The Analytics Engineer at Cartesia is responsible for creating and maintaining a reliable company-wide data source, integrating various data streams into consistent models and dashboards. Proficiency in SQL and experience with dbt or similar tools are essential, along with a strong background in analytics engineering or data engineering, particularly in a B2B SaaS environment. This role is ideal for someone with a collaborative mindset who can navigate complex data landscapes and establish clear metric definitions. As an early data hire, the engineer will play a crucial role in shaping the company's data infrastructure.

Joblaze insights

Listed yesterday — first seen on Joblaze September 19, 2026. Last confirmed on Cartesia's careers page September 19, 2026.
SQL appears in 54.4% of 79 comparable mid data engineering roles in United States; dbt appears in 32.9% of 79 comparable mid data engineering roles in United States.

Quick facts

Is the Analytics Engineer role remote?: No — this is an on-site role in San Francisco, CA, United States.
How much experience is required?: At least 5 years of relevant experience for this Analytics Engineer role.
Where is the role based?: Cartesia is hiring for this position in San Francisco, CA, United States.
What's the tech stack?: Joblaze extracted these technologies from the posting: SQL, dbt.
Does Cartesia sponsor work visas for this role?: Yes — the posting indicates visa sponsorship is available for the right candidate.
What seniority level is this role?: Cartesia targets mid-level candidates for this position.
Is this full-time or contract?: Full-time for this Analytics Engineer role at Cartesia.

From the original posting

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. This is an analytics engineering role focused on trustworthy systems and source-of-truth ownership.

🏦 401(k)

🦖 Your own personal Yoshi

Standard company text repeated across Cartesia's postings is omitted here.