Sales Engineer

Cube Dev; San Francisco (CA); On-site; USD 120,000 - 150,000; Full time

Job summary

A data analytics company in San Francisco is seeking a Sales Engineer to enhance customer relationships and drive technical solutions. Candidates should have over 5 years of experience in a similar role, showcasing deep proficiency in the modern data stack and proven success in SaaS environments. The ideal applicant will excel in communication with diverse stakeholders and have the ability to translate complex data challenges into innovative solutions. Familiarity with AI infrastructure and a background in analytics will be advantageous.

Qualifications

• 5+ years of experience in a technical customer-facing role.
• Proven track record supporting SaaS sales cycles with complex products.
• Fluency in cloud data warehouses and BI platforms.
• Strong understanding of data modeling concepts.

Responsibilities

• Become a subject-matter expert on data modeling and performance tuning.
• Partner with Account Executives for demos and technical engagements.
• Own the technical customer relationship and gather requirements.
• Capture and document customer feedback to influence product roadmap.

Skills

Sales Engineering

Solutions Consulting

Technical Customer Engagement

Deep fluency in modern data stack

Strong SQL skills

Exceptional communication

Tools

Snowflake

Redshift

BigQuery

Node.js

Python

LookML

Job description

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.