Customer Success Insights Engineer

Responsibilities

• 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.

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