Data Engineer – Data Architecture for Data Science & Machine Learning

Penn State University is seeking a Senior Data Engineer with deep expertise in database design, optimization, and data access strategies to support their data science and machine learning initiatives. The role involves architecting and optimizing data systems to empower data scientists in their research and application deployment.

Responsibilities:

• Design and maintain scalable, high-performance database solutions to support data science workflows and ML experimentation
• Partner with data scientists to understand data access patterns and develop storage strategies that accelerate analysis and model training
• Serve as the internal subject matter expert on PostgreSQL—including schema design, indexing, partitioning, and query optimization
• Evaluate and integrate alternative database technologies (e.g., MongoDB, Neo4j, Redis, Cassandra) where they provide clear advantages
• Lead efforts to optimize data pipelines for both structured and unstructured data used in algorithm development
• Ensure data integrity, security, and governance across storage systems
• Implement monitoring, automation, and performance-tuning tools for all database environments
• Advise on data lifecycle management—balancing accessibility for R&D with efficiency and compliance requirements

Requirements:

• 5+ years of experience in data engineering, database architecture, or related technical roles
• Expert-level proficiency in PostgreSQL (query tuning, schema design, indexing, partitioning, replication)
• Strong understanding of data modeling, normalization vs. denormalization tradeoffs, and query optimization
• Experience with non-relational databases (e.g., MongoDB, Cassandra, Neo4j, Redis, or DynamoDB)
• Familiarity with machine learning workflows and how data is consumed for training, evaluation, and deployment
• Experience with cloud database services (AWS RDS/Aurora, GCP Cloud SQL, Azure Database)
• Proficiency in SQL and one or more scripting languages (Python preferred)
• Excellent communication and collaboration skills—comfortable working closely with data scientists, ML engineers, and software developers
• Experience architecting hybrid data ecosystems spanning relational, NoSQL, and analytical databases
• Knowledge of data lake, warehouse, and feature store architectures (e.g., Snowflake, Redshift, BigQuery, Feast)
• Familiarity with ETL/ELT frameworks and data orchestration tools (e.g., Airflow, dbt)
• Bachelor's or Master's degree in Computer Science, Data Engineering, or a related field