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