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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 your location, experience, and the pay of employees in similar positions. The base salary range is 168,000 USD - 258,750 USD for Level 4, and 200,000 USD - 322,000 USD for Level 5. You will also be eligible for equity and benefits . Applications for this job will be accepted at least until August 21, 2026. 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.
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.
