Customer Success Insights Engineer

NVIDIA•Santa Clara, CA

7d•$168,000 - $322,000

About The Position

NVIDIA's Customer Success Business Insights team is seeking a Customer Success Insights Engineer to scale platform adoption through data and AI-native analytical solutions. The role surfaces how customers, partners, and developers adopt NVIDIA platforms and identifies opportunities to accelerate their success. The engineer will build and operate production data pipelines, apply AI agents across the engineering lifecycle, and convert ambiguous adoption questions into transparent metrics and self-service dashboards. The position also develops recurring analytical products to track platform adoption, developer engagement, and ecosystem health across NVIDIA software. It partners with internal teams and external platform vendors to source telemetry, validate data contracts, and establish measurement baselines, including for emerging channels where APIs change frequently and historical data may be lost. The role emphasizes trustworthy measurement: ensuring numbers are defensible, data sources are documented, and anomalies are investigated before findings reach customers or executives. The posting lists base salary ranges of $168,000–$258,750 for Level 4 and $200,000–$322,000 for Level 5, depending on location, experience, and comparable employee pay. The position is also eligible for equity and benefits.

Requirements

Bachelor of Science degree in Computing Science, Engineering, Math, or equivalent experience.
At least 8 years of experience in Data Engineering, Analytics Engineering, Business Intelligence, or similar roles building production data products.
Proven experience operationalizing LLMs into autonomous agents that can plan, use tools, and implement multi-step workflows, applied to real engineering work such as agent-assisted development, automated verification and review gates, or production agentic pipelines.
Deep expertise in Apache Spark, PySpark, Delta Lake, and Databricks Workflows.
Expert SQL and Python skills, including API-based data ingestion from enterprise systems and third-party platforms.
Experience integrating CRM and enterprise data, such as Salesforce or similar, with product telemetry into unified analytical models.
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.

Nice To Haves

Hands-on experience scaling Unity Catalog.
Background with NVIDIA AI technologies and platforms, or experience measuring developer ecosystems such as GitHub/GitLab telemetry, package registries, model hubs, or marketplace analytics.
Experience designing multi-agent or agentic engineering workflows, such as Claude Code, Codex, Cursor, or Nemotron, with verification and code review gates.
Active Databricks certifications, such as Data Engineer Professional or Generative AI Engineer Associate.
MS in Computer Science, Data Science, or equivalent experience in a related professional background.

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.
Use AI agents throughout the engineering lifecycle, including multi-agent build workflows, automated verification, and adversarial review gates before production or executive delivery.
Translate ambiguous leadership questions about adoption into measurable definitions, transparent metrics, and self-service dashboards, while communicating limitations and avoiding unsupported aggregation or claims.
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 telemetry, validate data contracts, and establish baselines before changes ship.
Operationalize measurement for emerging channels such as AI agent marketplaces, developer registries, and model hubs where APIs change frequently and uncollected history may be lost.
Champion data honesty by ensuring every number is defensible, every source is detailed, and anomalies are investigated before information reaches a customer.