Cartesia
Analytics Engineer
About this role
Cartesia is seeking an Analytics Engineer to establish the company's centralized data foundation. You'll build reliable warehouse models, create canonical metric definitions, and deliver trusted dashboards across product, billing, marketing, and operations—turning fragmented data sources into a single source of truth for the organization.
What you'll do
- Own end-to-end data pipelines from source systems through dbt models to canonical datasets and dashboards
- Identify and remediate data-quality issues across ETL, transformations, metric definitions, and reporting
- Build and maintain SQL-based warehouse models with proper testing, monitoring, lineage, and documentation
- Partner with Product, RevOps, Growth, and GTM to translate business requirements into durable data architecture
- Create self-serve dashboards for core metrics including activation, usage, billing, and customer health
- Audit and recommend improvements to the existing analytics stack and tooling
What they're looking for
- Expert SQL and dimensional modeling (historization, identity resolution)
- dbt or equivalent transformation framework (testing, orchestration, monitoring)
- Data warehouse architecture and best practices
- Product/billing/CRM/marketing analytics experience
- Stakeholder communication and translating ambiguous requirements into precise models
- End-to-end discrepancy investigation and root-cause analysis
- Dashboard and self-serve analytics design
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Cartesia
Cartesia builds multimodal AI models and voice AI platforms that power enterprise applications. The company is hiring for roles spanning customer support, enterprise deployments, inference infrastructure, internal developer tooling, and forward-deployed engineering to scale their AI solutions across production environments.
- Website
- cartesia.ai
Likely interview questions
- Walk us through a time you discovered a critical data-quality issue in production—how did you investigate it end-to-end and prevent recurrence?
- Describe your approach to designing a dimensional model for product usage and billing across multiple source systems.