Senior Data Engineer, Engineering Data Analytics

Job Title

Senior Data Engineer, Engineering Data Analytics

Role Summary

Responsible for building and maintaining cloud-based data platforms, pipelines, and curated analytics datasets that support engineering analytics, reporting, and AI-assisted insights for semiconductor product, manufacturing, and test data.

Work with engineering, IT, data, cloud, UI, and product teams to design data models, data pipelines, validation frameworks, and scalable architectures that turn engineering test logs and metrics into trusted analytics products.

Experience Level

Senior — 8+ years of relevant experience.

Responsibilities

Deliver production-ready data solutions and ensure analytics data is correct, performant, and usable by technical stakeholders.

• Build and evolve engineering analytics datasets, data models, and data products for product, manufacturing, and test data.
• Translate domain concepts into reliable data structures, metric logic, and validation rules.
• Own and improve curated data layers (prep/fact tables, silver/gold datasets, semantic views, analytics-ready outputs).
• Define and implement data quality checks, acceptance criteria, and validation frameworks for production analytics data.
• Collaborate with product engineering, UI, and data teams to convert ambiguous questions into scalable data solutions.
• Provide technical direction: define standards, review designs, and ensure maintainability.
• Optimize data pipelines and datasets for correctness, performance, scalability, reliability, and cost.
• Support AI-enabled analytics by building well-governed semantically clear datasets for LLMs, anomaly detection, prediction, and recommendations.

Requirements

Must-have technical skills and experience required to perform the role.

• Advanced SQL skills (window functions, CTEs, complex joins, aggregation patterns, query optimization, analytical query design).
• Strong Python skills or equivalent experience building data-intensive software systems.
• Experience designing data models, analytics datasets, 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, and validation rules.
• Proven ability to learn complex technical domains and detect semantic issues in data outputs.
• Ability to work cross-functionally with domain experts, engineers, product/UI, and data engineering teams while providing technical ownership.
• Interest in applied AI/ML and how trusted data foundations enable AI-based exploration and analytics.

Nice-to-have:

• Experience with semiconductor product engineering, test engineering, yield/manufacturing analytics, quality, or reliability data.
• Experience with cloud data platforms and lakehouse technologies such as S3, Athena, Glue, Redshift, EMR, Spark, Databricks, or Delta Lake.
• Experience with AI/ML-enabled analytics: LLMs, RAG, natural-language-to-SQL, feature engineering, anomaly detection, prediction, or recommendation systems.
• Experience building internal engineering analytics platforms or decision-support tools for technical users.

Education Requirements

Bachelor's or Master's degree in Computer Science, Computer Engineering, or Electrical Engineering — or equivalent practical experience.

About the Company

Company: NVIDIA

Headquarters: Santa Clara, California, USA

NVIDIA is a global leader in accelerated computing, renowned for its innovative solutions in AI and digital twins that transform diverse industries. The company specializes in networking technologies, providing end-to-end InfiniBand and Ethernet solutions for servers and storage that optimize performance and scalability. NVIDIA serves sectors such as high-performance computing, enterprise data centers, and cloud computing, constantly reinventing its products and services to stay ahead in the market.