Senior Data Engineer, Engineering Data Analytics

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, anomaly detection, prediction, and recommendations.
• Optimize data pipelines and analytics datasets for correctness, performance, scalability, reliability, and cost.

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 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.
• 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.

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 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.