Solutions Architect

About Cube Cube is the universal semantic layer that makes it easy to connect BI silos, embed analytics, and power data apps and AI with context. Cube enables teams to define consistent metrics once and deliver them everywhere, from BI tools like Tableau and Looker to custom applications and AI agents. About the Role As a Solutions Architect at Cube, you'll be the technical bridge between our product and 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 will have a direct impact on customer success. What You'll Do Technical Leadership & Architecture Design and architect end-to-end semantic layer solutions using Cube, integrating with customers' existing data warehouses (e.g., Snowflake, BigQuery, Redshift). Build comprehensive data models in YAML or JavaScript that define metrics, dimensions, and business logic to support data analysis and decision-making. Develop proof-of-concepts and technical demonstrations that showcase Cube's capabilities on customer data. Guide customers on best practices for data modeling, caching strategies, access control, and performance optimization. Customer Engagement Lead technical discovery sessions to understand customer data architecture, analytics requirements, and business objectives. Conduct hands-on workshops and training sessions to enable customer teams to use Cube effectively. Partner with Sales to provide technical expertise during the evaluation process. Serve as a trusted technical advisor throughout the customer lifecycle, from pre-sales through post-implementation. Solution Development Write complex SQL queries to analyze customer data and validate solution designs. Conduct data analysis to identify opportunities for optimization and architectural improvements. Build integrations between Cube and downstream tools (BI platforms, notebooks, custom applications). Create technical documentation, reference architectures, and implementation guides. Product Collaboration Provide customer feedback to Product and Engineering teams to influence the roadmap. Contribute to internal tooling and automation to improve solution delivery. Develop reusable patterns and frameworks for common implementation scenarios to facilitate efficient and consistent development. What You Bring Required Skills Expert-level SQL proficiency - You can write complex queries, optimize performance, and understand query execution plans. This is the foundational skill for success in this role. Strong data analysis capabilities - You understand how to explore data, identify patterns, validate metrics, and communicate insights. Programming experience in JavaScript OR Python - You're comfortable reading and writing code, working with APIs, and building data transformations. 3+ years in solutions architecture, data engineering, analytics engineering, or similar technical customer-facing roles. Deep understanding of modern data stack architecture (data warehouses, transformation tools, BI platforms). Experience with semantic layers, metrics layers, or BI modeling frameworks (LookML, dbt metrics, etc.). Strong communication skills - you can translate technical concepts for both technical and business audiences. Highly Valued Prior experience with Cube.js or similar semantic layer platforms. Background in analytics engineering or data platform roles. Experience with data modeling best practices and dimensional modeling. 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). Understanding of multi-tenancy, access control, and data governance requirements. Nice to Have Experience with embedded analytics or building data-powered applications. Knowledge of both JavaScript AND Python ecosystems. Contributions to open-source data projects. Familiarity with AI/LLM integration with semantic layers. What Success Looks Like Customers successfully deploy Cube into production with well-architected, performant solutions. High satisfaction scores from customers with technical guidance and support. Ability to handle complex, multi-source data modeling scenarios. Proactive identification of opportunities to expand Cube usage within customer organizations. Contributions to the internal knowledge base and solution patterns that benefit the entire team. Why Join Cube Work with cutting-edge semantic layer technology at the intersection of data engineering, analytics, and AI. Collaborate with a passionate team that includes the creators of the open-source Cube project. Make a direct impact on how thousands of companies organize and access their data. Competitive compensation. Remote-friendly culture with flexible work arrangements.