Senior Data Engineer, Engineering Data Analytics

NVIDIA•Santa Clara, CA

1d•$168,000 - $270,250

About The Position

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.

Requirements

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

Nice To Haves

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.

Responsibilities

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.