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
-
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.
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.
-
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.
-
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.
