Senior Data Engineer

The Senior Data Engineer will oversee backend data pipelines and massively parallel processing (MPP) database systems for the Mikulski Archive for Space Telescopes (MAST) at the Space Telescope Science Institute. Based in Baltimore, MD, this role offers a hybrid work arrangement with residency considerations and ITAR requirements, supporting high-performance data access for scientific research. The position carries a compensation range of USD 125,000 to 150,000 per year.

Responsibilities

• PostgreSQL and MPP platform performance and operations
• Build, maintain, and continuously improve data systems supporting scientific research, including relational databases and cloud-based lakehouse environments
• Ensure data accuracy, accessibility, observability, and reliability through proactive monitoring, alerting, and incident response
• Collaborate with scientists, data engineers, and cross-functional teams to translate requirements into robust, scalable platform solutions

Requirements

• Advanced expertise in PostgreSQL and Greenplum MPP
• Strong proficiency with Apache Airflow
• Hands-on experience with AWS cloud services
• Strong Python programming skills and proficiency in SQL and SQL performance tuning
• Experience designing, building, and optimizing data pipelines at scale
• Minimum of 8 years of relevant experience
• Bachelor’s or Master’s degree in computer science, information technology, or a related discipline

Technologies

• PostgreSQL
• Greenplum
• Apache Airflow
• AWS
• Python
• SQL
• Trino
• Apache Iceberg
• Lakehouse

Benefits

• Employer retirement contribution – direct STScI contribution of 10% of your salary from day one
• 12 days sick leave
• Up to 24 days of vacation
• 10 paid holidays
• Flexible work schedule with healthy work life balance
• Comprehensive medical, dental, vision, and prescription plans, and more

Growth opportunities

• Specialized technologies such as Trino for distributed querying, Apache Iceberg for lakehouse management, Greenplum or other MPP systems, and large-scale performance tuning
• Addressing complex challenges with high-volume scientific data, query optimization, and modern data infrastructure