Jobs at Sigma Computing

Lead customer onboarding and deployments for Sigma: define deployment paths, configure environments and data connections, coach users on BI/app best practices, optimize performance and security (SSO, RLS), manage milestones from kickoff to handoff, create documentation, and provide product feedback across Sales, CS, Support, and Product.

Provide customer-facing technical support for the Sigma SaaS platform: diagnose and resolve complex SQL and data issues, collaborate with product and engineering, develop diagnostic tools and best practices, participate in on-call rotation and cross-functional improvement projects.

Own end-to-end sales commission calculation, validation, and payout processing. Administer compensation platform and integrations (CRM, HRIS, ERP), manage quota/crediting rules, produce documentation and reports, and partner with Finance, Sales Ops, HR, and Data to improve accuracy and automate processes.

Manage strategic, high-value enterprise accounts to maximize Net Dollar Retention by driving product adoption, upsell/cross-sell, and reducing churn. Provide analytics and BI expertise, create success plans, advocate product needs to engineering, and deliver trainings and workshops to increase engagement and value realization.

Partner with the VP of Sales Engineering to run global operating rhythm, build reporting and dashboards in Sigma, optimize pre-sales funnel and POC lifecycle, lead global capacity and headcount planning, and align SE requirements across Sales, Product, RevOps, and Comp/Systems.

Manage a high-volume portfolio of mid-market SaaS customers using scalable engagement models. Drive adoption and time-to-value via self-serve onboarding, training, and automation. Monitor customer health metrics to identify churn risk and expansion opportunities, run cohort and segmentation analysis, deliver targeted communications, and collaborate with Sales, Support, and Product to enable fast issue resolution and scalable playbooks.

Lead the analytical foundation for GTM pipeline generation: define TAM/SAM, identify buying signals, measure program impact, and build cross-functional pipeline programs with Sales, BD, Marketing, and Product. Drive data-led sales plays, create supporting assets, and manage cross-functional execution to convert targeted accounts into pipeline and ARR outcomes.

Lead design and execution of sales productivity and ramp programs for enterprise and commercial teams. Build and maintain ramp reporting dashboards, analyze sales KPIs, collaborate cross-functionally with GTM stakeholders, manage enablement content and projects, and present insights to sales leadership to accelerate seller productivity.

Design, build, and operate production data pipelines and architectures across Snowflake and Databricks. Scale demo data across cloud warehouses, partner with infrastructure for high availability, implement data governance, and collaborate cross-functionally to support analytics and GTM use cases.

Design and maintain production data pipelines and architecture across Snowflake and Databricks, scale Terraform deployments, implement data governance, build demo data processes, and collaborate cross-functionally to support analytics and product use cases.

Lead customer marketing: build a repeatable proof engine to turn customer outcomes into case studies, videos, reviews, and references; run advocacy, CAB, speaker programs, event customer voice, and distribute proof across GTM using AI-first tooling.

Lead technical onboarding and deployment for new customers: configure environments and data connections, coach users, implement BI best practices, optimize performance, secure SSO/row-level access, enable embedded analytics and migrations, manage milestones and handoffs, and produce documentation and feedback for product and support teams.

Lead go-to-market strategy and messaging for Sigma Apps, translating product capabilities into buyer-focused narratives. Drive feature launches, sales enablement, competitive analysis, thought leadership, and customer case studies while partnering with Product, Engineering, Design, Marketing, and Sales to shape roadmap and field communications.

Manage and grow strategic enterprise accounts in the Upper Midwest by driving product adoption, maximizing Net Dollar Retention, delivering analytics-led guidance, advocating customer needs to product teams, creating success plans, and running training and enablement to increase engagement and reduce churn.

Lead AI product marketing for Sigma: own positioning, go-to-market strategy, and launches for AI capabilities (LLMs, agentic workflows, coding agents). Partner with product, engineering, and sales to create messaging, enablement, thought leadership, and competitive intelligence that drives pipeline and wins enterprise AI deals.

Lead editorial strategy and run the Sigma blog end-to-end: edit technical blog content, enforce brand and editorial standards, manage AEO/SEO operations, coach contractors and internal SMEs, support executive ghostwriting, and collaborate with PMs and engineers to ensure accuracy. Drive content strategy, mentor contributors, and scale systems for consistent, high-quality content and organic growth.

Lead and grow a team of Support Engineers for a cloud data warehouse analytics product (Sigma). Hire, mentor, and train staff; own support metrics (CSAT, IR, SLA, TTR); improve processes; collaborate cross-functionally; analyze support data to drive product and operational improvements.

Sell Sigma's analytics platform to new customers in the Bay Area. Manage the full sales cycle, generate pipeline, maintain Salesforce records, collaborate with partners, and meet quota. Engage consultatively with data and analytics stakeholders to close deals and build customer advocates.

Lead technical presales by discovering customer needs, building and presenting customized BI demos and prototypes, responding to RFPs/RFIs, supporting integrations and data migrations, and managing multiple customer engagements to drive adoption and sales.

Design, prototype, and productionize AI systems and infrastructure—recommendations, natural language interfaces, agentic workflows—partnering with product and engineering to scale ML across the full lifecycle (data curation, training, deployment, monitoring).