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Cartesia is hiring an Analytics Engineer to build and own the company's source of truth for data across product, billing, CRM, marketing, and operations. This is an early, foundational data hire at a well-funded AI company founded by Stanford PhD researchers who invented State Space Models.
You will own the complete path from source systems to canonical datasets, metrics, and dashboards. Your responsibilities include identifying and fixing data-quality issues across pipelines, models, definitions, and reporting surfaces; building and maintaining reliable warehouse models using SQL and dbt; establishing clear metric definitions with tests, lineage, monitoring, and documentation; and partnering closely with Product, Engineering, RevOps, Growth, and GTM teams to translate business concepts into durable data models.
You'll create trusted dashboards for core company metrics such as activation, usage, billing, customer health, and marketing performance. A key focus is making common data questions self-serve while ensuring dashboards reuse canonical logic rather than duplicating it. You'll audit the existing data stack and recommend pragmatic improvements or overhauls to ETL and analytics tooling. You'll also educate the company on how to use the source of truth and establish standards for how new metrics and dashboards should be created.
Cartesia is an in-person team with offices in San Francisco, London, and Bangalore. The company provides visa sponsorship on a case-by-case basis and emphasizes fast execution, high quality, and an open, inclusive culture.
REQUIREMENTS:
- 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
- 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 especially valuable
- Strong judgment about when a problem belongs in a source system, pipeline, warehouse model, semantic layer, or dashboard
- Ability to investigate discrepancies end-to-end and prevent them from recurring
- Strong stakeholder instincts and ability to make ambiguous business concepts precise
- 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
