Data Engineer - DoD Health Data Pipelines

Systems Planning and Analysis, Inc.; Alexandria (VA); On-site; USD 120,000 - 146,000; Full time

Job summary

Intrepid, an SPA Company, is seeking a Data Engineer to support the Defense Health Agency Chief Data and Analytics Office. You will design scalable data pipelines, build data products, and collaborate with cross-functional teams to enable enterprise reporting and decision-making.

Ideal candidates have 3–10 years of data engineering and strong Python/SQL skills, with experience in Spark and Databricks, and a Secret clearance.

Qualifications

• Active Secret clearance required.
• Bachelor's degree in CS/IS/SE or related field.
• 3–10 years of data/ software engineering or related technical experience.
• Strong proficiency with Python and SQL.

Responsibilities

• Design, develop, and maintain scalable data pipelines using Spark, Python, and SQL in a Databricks environment.
• Integrate and transform diverse health, financial, and operational datasets into reliable data products.
• Develop data models and analytics datasets to support enterprise reporting and decision-making.
• Build and maintain API-based data integrations for ingesting and exchanging data across systems.
• Monitor, troubleshoot, and optimize workflows and pipelines for performance and reliability.
• Implement data validation, quality checks, governance, and lineage practices.
• Contribute to automated testing, code reviews, and technical documentation.
• Collaborate with engineers, analysts, and stakeholders to translate data needs into solutions.

Skills

Python

SQL

Data modeling

ETL/ELT

REST APIs

Git workflows

Communication

Education

Bachelor's degree in Computer Science, Information Systems, Software Engineering, or related field

Tools

Apache Spark

Databricks

Git-based workflows

AWS

Job description

Intrepid, an SPA Company, is seeking a Data Engineer to support the Defense Health Agency Chief Data and Analytics Office. You will design scalable data pipelines, build data products, and collaborate with cross-functional teams to enable enterprise reporting and decision-making.

Ideal candidates have 3–10 years of data engineering and strong Python/SQL skills, with experience in Spark and Databricks, and a Secret clearance.