Data Engineer (AI & Data)

Princeton IT Services, Inc. seeks an experienced Data Engineer to design, develop, and maintain scalable data engineering solutions. The role focuses on building and optimizing data pipelines and workflows using Databricks, PySpark, SQL, Delta Lake, and cloud platforms, while supporting data quality, governance, and reliable data products.

Responsibilities:

• Design, develop, and maintain scalable ETL/ELT pipelines using Databricks, PySpark, and SQL
• Build data ingestion and transformation frameworks for large-volume structured and semi-structured datasets
• Implement Delta Lake patterns, data quality validations, exception handling, and data reconciliation processes
• Optimize Spark jobs and SQL queries for performance, scalability, and cost efficiency
• Work with Unity Catalog for data governance, access control, and data management
• Develop and manage workflows using Azure Data Factory (ADF) and Databricks Workflows
• Implement CI/CD practices for data pipeline deployment and management
• Collaborate with data analysts, architects, and source-system teams to deliver reliable and high-quality data products

Requirements:

• 5+ years of experience in Data Engineering
• Strong hands-on experience with Databricks, PySpark, and SQL
• Expertise in Delta Lake, ETL/ELT, and large-scale data processing
• Experience with Unity Catalog and data governance
• Working knowledge of Azure/AWS cloud platforms
• Experience with Azure Data Factory (ADF) and Databricks Workflows
• Knowledge of CI/CD processes and pipeline deployment
• Strong understanding of Spark performance tuning and optimization
• Excellent problem-solving and analytical skills
• Experience working with healthcare or life sciences data
• Knowledge of data quality frameworks and reconciliation processes
• Experience building enterprise-scale data pipelines and curated data products