Senior Data Engineer

Senior Data Engineer at Space Telescope Science Institute will manage backend data pipelines and MPP database systems for the Mikulski Archive for Space Telescopes (MAST), enabling high-performance data access, with a hybrid work arrangement in Baltimore, MD, residency/ITAR requirements, a salary range of USD 125,000 - 150,000 per year, and a minimum of 8 years of experience plus a Bachelor's or Master's degree in computer science, information technology, or a related discipline.

Responsibilities

• Manage PostgreSQL and MPP platform performance and operations
• Design, implement, deploy, monitor, and troubleshoot complex data pipelines using Apache Airflow to process, transform, and load large-scale datasets reliably
• Build, maintain, and continually improve data systems supporting scientific research, including relational databases and cloud-based lakehouse architectures
• 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
• Extensive experience designing, building, and optimizing data pipelines at scale

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 your first day
• 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, prescription plans, and more

Growth opportunities

• Specialize in Trino for distributed querying
• Work with Apache Iceberg for lakehouse management
• Explore Greenplum or other MPP systems and large-scale performance tuning
• Tackle complex challenges with high-volume scientific data, query optimization, and modern data infrastructure