Solutions Architect, Cube

• 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.
• 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

Desirable

• 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
• 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
• 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
• 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
• 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 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