The NVIDIA Operations organization is seeking an experienced data engineering professional for the position of Senior Data Engineer, Engineering Data Analytics. As a member of our team, you will be an integral part of building cloud-based data platforms that support engineering analytics, reporting, and AI-assisted insights! You will work on data tools used in the testing and analysis of semiconductor chips, boards, systems, and servers. Our team develops an in-house suite of tools that process engineering logs and test data into trusted data platforms, analytics products, and custom visualizations for large-scale data analysis.
This role will help turn complex engineering data into reliable information, actionable insights, and business results. You will partner with engineering, IT, data, cloud, UI, and implementation teams to design data models, data pipelines, curated analytics layers, and scalable architectures based on data sources, data locations, and engineering use cases.
What you will be doing:
Build and evolve trusted engineering analytics datasets, data models, and data products for semiconductor product, manufacturing, and test data.
Translate complex domain concepts into reliable data structures, metric logic, validation rules, and reusable analytics layers.
Own and improve curated data layers, including prep/fact tables, silver/gold datasets, semantic views, and analytics-ready outputs.
Partner with product engineering, UI, and data engineering teams to turn ambiguous engineering questions into scalable data solutions.
Define data quality checks, acceptance criteria, and validation frameworks for production analytics data.
Provide technical direction by defining standards, reviewing designs, and ensuring long-term maintainability.
Help guide the evolution of data architecture across modern warehouse, data lake, and lakehouse technologies such as Redshift, S3/Athena, and Databricks.
Support AI-enabled analytics by building well-governed, semantically clear datasets for AI-based exploration, natural-language analytics, anomaly detection, prediction, and recommendations.
Optimize data pipelines and analytics datasets for correctness, performance, scalability, reliability, and cost.
What we need to see:
Strong SQL skills, including advanced SQL concepts such as window functions, CTEs, complex joins, aggregation patterns, query optimization, and analytical query design.
Strong Python skills, or equivalent experience building data-intensive software systems.
Experience designing data models, analytics datasets, data products, or application data layers.
Experience building or owning production data pipelines, data platforms, or analytics systems.
Strong understanding of data correctness, table grain, lineage, metric definitions, validation rules, and data quality standards.
Ability to learn complex technical domains and identify when data outputs are technically valid but semantically wrong.
Ability to work multi-functionally with domain experts, engineers, product/UI teams, and data engineering teams while providing technical ownership and judgment.
Interest in applied AI/ML and how trusted data foundations enable AI-based exploration, anomaly detection, predictive analytics, and recommendations.
Bachelor’s or Master’s degree in Computer Science or Computer Engineering or Electrical Engineering (or equivalent experience) and 8+ years of relevant experience
Ways to stand out from the crowd:
Experience with semiconductor product engineering, test engineering, yield analytics, manufacturing analytics, quality, reliability, or hardware engineering data is a strong plus!
Experience with modern cloud data platforms, data lake, or lakehouse technologies such as S3, Athena, Glue, Redshift, EMR, Spark, Databricks, Delta Lake, or similar technologies.
Experience with AI/ML-enabled analytics, including LLMs, RAG, AI-based data exploration, natural-language-to-SQL, feature engineering, anomaly detection, prediction, or recommendation systems.
Experience building engineering analytics platforms, internal data products, or decision-support tools for technical users.
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
- Strong SQL skills, including window functions, CTEs, complex joins, aggregation patterns, and query optimization.
- Strong Python skills or equivalent experience building data-intensive software systems.
- Experience designing data models, analytics datasets, data products, or application data layers.
- Experience building or owning production data pipelines, data platforms, or analytics systems.
- Strong understanding of data correctness, table grain, lineage, metric definitions, validation rules, and data quality standards.
- Ability to learn complex technical domains and identify when data outputs are technically valid but semantically wrong.
- Ability to work cross-functionally with domain experts, engineers, product/UI teams, and data engineering teams while providing technical ownership and judgment.
- Bachelor's or Master's degree in Computer Science, Computer Engineering, or Electrical Engineering (or equivalent experience).
- 8+ years of relevant experience.
- Experience with semiconductor product engineering, test engineering, yield/manufacturing analytics, quality, reliability, or hardware engineering data.
- Experience with cloud data platforms and lakehouse technologies (S3, Athena, Glue, Redshift, EMR, Spark, Databricks, Delta Lake).
- Experience with AI/ML-enabled analytics (LLMs, RAG, natural-language-to-SQL, anomaly detection, prediction, recommendation systems).
- Experience building engineering analytics platforms, internal data products, or decision-support tools for technical users.
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.”
