Customer Success Insights Engineer

NVIDIA's Customer Success Business Insights team is looking for a Customer Success Insights Engineer to scale platform adoption through data and AI-native analytical solutions. You'll surface how customers, partners, and developers adopt NVIDIA platforms — and where we can accelerate their success. NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us! What you’ll be doing:   • Design, build, and operate automated data collection and transformation pipelines across enterprise systems, vendor APIs, and public developer platforms into Databricks, with data quality gates, freshness monitoring, and fail-safe behavior built in from day one.   • Use AI agents throughout the engineering lifecycle: multi-agent build workflows, automated verification, and adversarial review gates before anything reaches production or an executive audience.   • Turn ambiguous adoption questions from leadership into measurable definitions, transparent metrics, and self-serve dashboards, including the caveats: knowing when signals must not be summed, funneled, or over-claimed.   • Develop and maintain executive dashboards and recurring analytical products that track platform adoption, developer engagement, and ecosystem health across NVIDIA software.   • Partner with Product, Marketing, Sales Operations, and external platform vendors to source new telemetry, validate data contracts, and establish baselines before changes ship, so every initiative gets a measured before and after.   • Operationalize measurement for emerging channels (AI agent marketplaces, developer registries, model hubs) where APIs change weekly and un-captured history is lost forever.   • Champion data honesty as a product feature: every number defensible, every source detailed, every anomaly investigated before it reaches a customer.   What we need to see:   • BS degree in Computing Science, Engineering, Math or equivalent experience.   • 8+ years of experience in Data Engineering, Analytics Engineering, Business Intelligence, or similar roles in building production data products.   • Agentic AI & LLM Mastery: Proven experience operationalizing Large Language Models (LLMs) into autonomous agents that can plan, use tools, and implement multi-step workflows, applied to real engineering: agent-assisted development, automated verification and review gates, or agentic pipelines that shipped to production.   • Databricks Mastery: Proven deep expertise in Apache Spark, PySpark, Delta Lake, and Databricks Workflows. Hands-on experience scaling Unity Catalog is highly preferred.   • Expert SQL and Python, including API-based data ingestion from enterprise systems and third-party platforms.   • Experience integrating CRM and enterprise data (Salesforce or similar) with product telemetry into unified analytical models.   • A track record of building executive-facing dashboards and analytical narratives that leaders trust and act on.   • Excellent communication, stakeholder management, analytical, and problem-solving skills.   Ways to stand out from the crowd:   • Background with NVIDIA AI technologies and platforms, or measurement of developer ecosystems (GitHub/GitLab telemetry, package registries, model hubs, marketplace analytics).   • Experience designing multi-agent or agentic engineering workflows (Claude Code, Codex, Cursor, Nemotron, or similar) with verification and code review gates.   • Active Databricks Certifications (e.g., Data Engineer Professional, Generative AI Engineer Associate).   • MS in Computer Science, Data Science, or equivalent experience in a related professional background.   Your base salary will be determined based on you