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.