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About the Job
As a Data Engineer on the Smart Manufacturing and Automation team, you will design and implement data models that transform raw semiconductor manufacturing data into analytics-ready assets. Using modern lakehouse technologies (Spark, Apache Iceberg, Delta Lake, and OLAP SQL Engines), you will build ETL pipelines that integrate data from shop floor systems spanning ISA-95 Levels 0-4, processing Petabytes of data.
This role requires understanding of manufacturing data standards including ISA-95 and SEMI standards such as E90 (Substrate Tracking), E10 (Equipment Reliability, Availability, Maintainability, and Utilization), E116, etc. You will translate these domain concepts into scalable data structures that serve downstream analytics and reporting.

Key Responsibilities

• Design and implement data models for manufacturing data domains including equipment performance, substrate tracking, and production metrics, electrical test, etc.
• Build ETL/ELT pipelines using PySpark and SQL to load data into Iceberg and Delta Lake tables with proper historization and auditability
• Develop data transformations aligned with ISA-95 hierarchy and SEMI standards
• Create business vault and information marts that translate raw manufacturing data into analytics-ready data products
• Implement data quality checks and expectations within pipelines to ensure accurate, reliable data
• Collaborate with Manufacturing Engineering and Analytics teams to understand data requirements and deliver solutions
• Document data models, transformation logic, and business rules
• Optimize query performance by optimizing partitioning, clustering, and/or indexing strategies in various database technologies, Delta, and Iceberg.

Minimum Requirements

• Bachelor's degree in Computer Science, Data Science, or related field
• 8+ years of experience in data engineering or analytics
• Strong SQL and Python/PySpark for data transformations
• Experience with OLAP databases such as Redshift, BigQuery, Druid, Clickhouse, StarRocks, etc.
• Experience with lakehouse table formats: Apache Iceberg, Delta Lake, or similar
• Understanding of ETL/ELT patterns and data pipeline development
• Familiarity with Airflow or similar orchestration tools

Preferred Qualifications

• Data Vault 2.0 modeling methodology (Hubs, Links, Satellites, PITs, Bridges)
• Knowledge of ISA-95 hierarchy and/or SEMI standards
• Data quality frameworks (Great Expectations or similar)
• Experience in semiconductor or discrete manufacturing environments
• Databricks experience (Delta Live Tables, Unity Catalog)