Senior Data Engineer, Engineering Data Analytics

NVIDIA offers equity and a benefits package for this onsite Senior Data Engineer role in Santa Clara, CA. You will build cloud based data platforms and analytics for engineering data analytics, with a focus on data models, pipelines, and AI enabled insights to advance semiconductor product and manufacturing analytics.

Responsibilities

• Develop and evolve trusted engineering analytics datasets, data models, and data products for semiconductor product, manufacturing, and test data.
• Translate complex domain concepts into robust data structures, metric logic, validation rules, and reusable analytics layers.
• Own and enhance curated data layers including prep and fact tables, silver and gold datasets, semantic views, and analytics ready outputs.
• Collaborate with product engineering, UI, and data engineering teams to convert ambiguous engineering questions into scalable data solutions.
• Define data quality checks, acceptance criteria, and validation frameworks for production analytics data.
• Provide technical direction by establishing standards, reviewing designs, and ensuring long term maintainability.
• Support the evolution of data architecture across modern warehouse, data lake, and lakehouse technologies such as Redshift, S3/Athena, and Databricks.
• Enable 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 efficiency.

Requirements

• Strong SQL skills including advanced 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.
• Solid understanding of data correctness, table grain, lineage, metric definitions, validation rules, and data quality standards.
• Ability to learn complex technical domains and identify when outputs are technically valid but semantically incorrect.
• Ability to work cross 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 work.

Technologies

• Python
• SQL
• Redshift
• S3
• Athena
• Glue
• EMR
• Spark
• Databricks
• Delta Lake

Benefits

• Equity
• Benefits

Ways to Stand Out

• 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 and lakehouse technologies such as S3, Athena, Glue, Redshift, EMR, Spark, Databricks, Delta Lake or similar
• 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 technically oriented users