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

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.

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.

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.