Sales Engineer

About Cube

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 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'll Do

• Become the go-to subject-matter expert on semantic layers, data modeling, performance tuning, and modern data and AI stacks — including how Cube enables Agentic Analytics workflows.
• Partner with Account Executives to deliver tailored demos, technical deep dives, and proof-of-concept engagements that translate complex data challenges into compelling, elegant solutions.
• Own the full technical customer relationship: requirements gathering, solution architecture, integrations, and success criteria for pilots and POCs.
• Translate enterprise data challenges into solutions that leverage Cube's semantic layer alongside BI tools, data warehouses, AI agents, and governance systems.
• Capture and document customer feedback to directly influence the product roadmap and partner integration strategy.
• Collaborate with Account Executives on RFP/RFI responses.
• Generate pipeline and establish thought leadership by writing technical blog posts, delivering webinars, and engaging the Cube community.

Who You Are

• 5+ years of experience in Sales Engineering, Solutions Consulting, Solutions Architecture, or a closely related technical customer-facing role.
• Proven track record supporting SaaS sales cycles with complex technical products — ideally in the data, analytics, or AI infrastructure space.
• Deep fluency in the modern data stack: cloud data warehouses (Snowflake, Redshift, BigQuery), data transformation tools, semantic or metrics layers, and BI platforms.
• Strong SQL skills and a solid grasp of data modeling concepts, including experience translating business requirements into scalable data architectures.
• Exceptional communicator who can engage engineers, data leaders, and C-suite stakeholders with equal clarity and credibility.
• Entrepreneurial mindset: you thrive in fast-moving startup environments, solve problems proactively, and work effectively across product, engineering, and sales.

Nice-to-Haves

• Hands-on experience with semantic or metrics layers — Cube, LookML, MetricFlow, dbt Semantic Layer, or similar.
• Familiarity with legacy OLAP technologies (Microsoft SSAS, Oracle Essbase, SAP BW) and the ability to position modern alternatives.
• Proficiency in at least one high-level programming language: Node.js, Python, Ruby, Java, Scala, or similar.
• Experience building or contributing to solutions that integrate AI agents, LLMs, or agentic workflows with data infrastructure.
• Background in analytics consulting or data engineering — understanding the full data pipeline from ingestion to insight.
• Active presence in the data community: published content, conference talks, open-source contributions, or a strong professional network.