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.
You will also be eligible for equity and benefits.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.Skills Required
- BS in Computer Science, Engineering, Math or equivalent experience
- 8+ years in Data Engineering, Analytics Engineering, Business Intelligence, or similar roles building production data products
- Proven experience operationalizing Large Language Models (LLMs) into autonomous/agentic systems
- Deep expertise in Databricks, Apache Spark, PySpark, and Delta Lake (Databricks Workflows)
- Hands-on experience scaling or working with Unity Catalog
- Expert-level SQL and Python, including API-based data ingestion from enterprise systems and third-party platforms
- Experience integrating CRM/enterprise data (e.g., Salesforce) with product telemetry into unified analytical models
- Proven track record building executive-facing dashboards and analytical narratives
- Strong communication, stakeholder management, analytical, and problem-solving skills
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
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Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
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Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
NVIDIA Insights
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
