Solutions Architect

Cube Dev; A distancia; MXN 1,260,000 - 1,801,000; Jornada completa

Descripción de la vacante

A leading data technology company is seeking a Solutions Architect to design semantic layer solutions, lead customer engagement, and analyze data with strong SQL skills. The ideal candidate has 3+ years of experience in solutions architecture or data engineering, with proficiency in JavaScript or Python and excellent communication skills. This position offers competitive compensation and the opportunity to work remotely in Mexico.

Servicios

Competitive compensation

Remote-friendly culture

Flexible work arrangements

Formación

• 3+ years in solutions architecture, data engineering, or analytics engineering.
• Deep understanding of modern data stack architecture.
• Experience with semantic layers or BI modeling frameworks.

Responsabilidades

• Design and architect end-to-end semantic layer solutions.
• Lead technical discovery sessions with customers.
• Write complex SQL queries and analyze customer data.

Conocimientos

Expert-level SQL proficiency

Strong data analysis capabilities

Programming experience in JavaScript OR Python

Strong communication skills

Descripción del empleo

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.

With 19,000+ Git stars and 13,000+ community members, Cube is trusted by companies like SecurityScorecard, Webflow, The Linux Foundation, Cloud Academy, and SamCart. Our platform empowers AI agents with a universal semantic foundation — enabling autonomous analytics at scale while maintaining the same consistency, security, and performance across BI tools, spreadsheets, and embedded applications.

What you will 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 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.

Who you are

• 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.