C- Work from
- United States
- Contract
- Full-time
- Posted
- 9 November 2026
- Apply by
- 8 January 2027
If you're fascinated by building core data and AI infrastructure, the kind that powers analytics at the world's most advanced technology companies, but want the agility and ownership of a startup, Cube is where you'll thrive.
At Cube, we're redefining how organizations deliver, consume, and automate data and analytics across teams, tools, and AI agents. Our mission is to enable Agentic Analytics, where AI agents work alongside humans on a shared semantic foundation.
With 19,000+ GitHub stars and 13,000+ community members, Cube is trusted by 400+ companies, including Maersk, Kimberly-Clark, Freshworks, Patagonia, Webflow, Brex, Deel, Tubi, Walmart, and Drata. Our platform empowers AI agents with a universal semantic foundation, enabling autonomous analytics at scale while maintaining consistency, security, and performance across BI tools, spreadsheets, and embedded applications.
As a Solutions Architect at Cube, you'll be the technical bridge between our product and our customers' data infrastructure. You'll work hands-on with prospects and customers across industries to design, implement, and optimize semantic layer architectures that solve complex data challenges. This is a highly technical, customer-facing role where your SQL expertise and data analysis skills have a direct impact on customer success.
Responsibilities
- Design and architect end-to-end semantic layer solutions using Cube, integrating with customers' existing data warehouses (Snowflake, BigQuery, Redshift).
- Build comprehensive data models in YAML or JavaScript that define metrics, dimensions, and business logic.
- Develop proof-of-concepts and technical demonstrations on customer data, and lead technical discovery sessions to understand data architecture and requirements.
- Guide customers on best practices for data modeling, caching strategies, access control, and performance optimization.
- Run hands-on workshops and training to enable customer teams, and partner with Sales to provide technical expertise during the evaluation process.
- Write complex SQL to analyze customer data and validate solution designs, and build integrations between Cube and downstream BI tools, notebooks, and custom applications.
- Serve as a trusted technical advisor across the customer lifecycle, and bring customer feedback back to Product and Engineering to influence the roadmap.
Nice to have
- Prior experience with Cube.js or similar semantic layer platforms.
- Background in analytics engineering or data platform roles, and familiarity with dimensional modeling best practices.
- Familiarity with REST/GraphQL APIs and how applications consume analytics.
- Knowledge of caching strategies and performance optimization for analytics workloads.
- Experience with cloud data warehouses (Snowflake, BigQuery, Databricks, Redshift) and an understanding of multi-tenancy, access control, and data governance.
- Experience with embedded analytics or building data-powered applications.
- Familiarity with AI/LLM integration with semantic layers, and contributions to open-source data projects.
This role is customer-facing and involves occasional travel to customer sites and company events.
We're a fully remote company based in San Francisco. You can work from anywhere and join our highly collaborative team.
Eligibility
- Expert-level SQL proficiency: you can write complex queries, optimize performance, and understand query execution plans.
- Strong data analysis skills: you explore data, identify patterns, validate metrics, and communicate insights.
- Programming experience in JavaScript or Python, comfortable working with APIs and building data transformations.
- 3+ years in solutions architecture, data engineering, analytics engineering, or a similar technical customer-facing role.
- Deep understanding of the modern data stack: data warehouses, transformation tools, and BI platforms.
- Experience with semantic or metrics layers, or BI modeling frameworks (LookML, dbt metrics, or similar).
- Strong communication skills: you can translate technical concepts for both technical and business audiences.
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