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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.

We're looking for a Technical Customer Success Manager who can operate at the intersection of data engineering and business strategy. This is not a traditional CSM role where you send check-in emails and track NPS scores. You'll be the strategic partner for a portfolio of customers — helping them get real value from Cube, diagnosing technical problems, running QBRs with data leaders, and ensuring they renew and grow.

The "technical" in the title is real. Our customers are data engineers, analytics engineers, and engineering leaders. They talk in SQL, think in data models, and evaluate you on whether you actually understand their stack. You need to be credible in those conversations while also being the person who can walk into a QBR with a VP of Data and connect platform metrics to business outcomes.

This role reports to the Head of Customer Success.

• Own customer outcomes. Manage a portfolio of mid-market and enterprise accounts end-to-end — from onboarding through renewal. You're accountable for implementation, retention, adoption, and expansion within your book of business.
• Run strategic QBRs. Prepare and deliver quarterly business reviews that go beyond usage dashboards. You'll analyze deployment health, surface adoption gaps, build ROI narratives, and give customers a clear roadmap for getting more value from Cube.
• Get technical. Understand how customers have modeled their data in Cube, diagnose performance issues (pre-aggregation strategy, query optimization, caching behavior), and advise on best practices. You won't build data models day-to-day, but you need to read them, reason about them, and guide customers toward better architecture.
• Be the voice of the customer internally. Translate customer feedback into actionable product insights. Work closely with engineering, product, and solutions architecture to resolve issues and influence the roadmap.
• Drive adoption of new capabilities. Cube's platform is evolving fast — Analytics Chat, embedded agentic analytics, MCP integration, workbooks and dashboards. You'll help customers understand what's new, why it matters for their use case, and how to adopt it.
• Manage renewals and identify expansion opportunities. You'll own the commercial relationship alongside our sales team. This means understanding contract timelines, building the business case for renewal, and spotting natural expansion paths — more users, embedded analytics, additional environments.

Must-haves:

• 5+ years in a customer-facing role at a data, analytics, or developer-tools company (CSM, technical account manager, solutions consultant, or similar)
• Strong SQL skills — you can write queries with window functions, CTEs, and cohort logic, and you can read someone else's SQL and spot what's wrong
• Experience working with technical stakeholders (data engineers, analytics engineers, engineering managers) and executive stakeholders (VP/Head of Data, CTO) in the same account
• Track record of managing renewals and driving account growth in a SaaS environment
• Ability to run a QBR that a data leader would actually find valuable — business narrative, not just metrics
• Familiarity with the modern data stack: cloud data warehouses (Snowflake, BigQuery, Redshift, Databricks), transformation tools (dbt), and BI/analytics tools
• Excellent written and verbal communication — you can explain a pre-aggregation strategy to a data engineer and explain ROI to a VP in the same meeting
• Based in the US with availability for overlap with US business hours

Nice-to-haves:

• Experience with Cube or semantic layer concepts (metrics layers, governed data models, headless BI)
• Background in analytics engineering, data engineering, or BI — you've been on the practitioner side
• Familiarity with embedded analytics use cases and multi-tenant architectures
• Experience with API-first or developer-tool products where the buyer is technical
• Understanding of AI/LLM integrations in the data stack (MCP, text-to-SQL, agentic workflows)
• Comfort with light data modeling, YAML/code-based configuration, or git workflows

• Competitive salary and generous stock options in a growing company
• Unlimited PTO — we actually use it
• Remote-first culture with coworking space stipend if you prefer not working from home
• Team offsites several times a year (locations rotate between the US and Europe)
• Professional development budget — training courses, books, conferences
• The chance to work on a product that sits at the center of the modern data stack and the AI-native future

1. Introductory call — 30 min with the recruiting team
2. Hiring manager conversation — 45 min to discuss your experience, approach, and what you're looking for
3. Take-home exercise — prepare and present a mock QBR using a provided data pack (~3–4 hours of prep + 30 min live presentation)
4. Cross-functional interviews — conversations with team members from CS, Sales, and Engineering
5. Offer