C- Work from
- United States
- Contract
- Full-time
- Posted
- 8 November 2026
- Apply by
- 7 January 2027
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.
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+ 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 making sure they renew and grow. The "technical" in the title is real: our customers are data engineers, analytics engineers, and engineering leaders who talk in SQL and think in data models, and they evaluate you on whether you actually understand their stack. This role reports to the Head of Customer Success.
Responsibilities
- Own customer outcomes. Manage a portfolio of mid-market and enterprise accounts end to end, from onboarding through renewal, accountable for implementation, retention, adoption, and expansion.
- Run strategic QBRs that go beyond usage dashboards: 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.
- Be the voice of the customer internally, translating feedback into product insights and working with engineering, product, and solutions architecture to resolve issues.
- Drive adoption of new capabilities such as Analytics Chat, embedded agentic analytics, MCP integration, workbooks, and dashboards.
- Manage renewals and identify expansion opportunities, owning the commercial relationship alongside our sales team.
Nice to have
- Experience with Cube or semantic layer concepts (metrics layers, governed data models, headless BI).
- Background in analytics engineering, data engineering, or BI, having 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.
This role is customer-facing and involves occasional travel to customer sites and company events.
We're a fully remote company based in San Francisco, hiring for this role in the United States. You can work from anywhere in the US and join our highly collaborative team.
Eligibility
- 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 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.
- A 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.
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