Data Engineer: DoD Health Data Pipelines & Spark

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

Benefits offered by this job

Competitive compensation

Industry-leading 401k contribution

Job summary

Intrepid, an SPA Company, is seeking a Data Engineer to support the Defense Health Agency and build an enterprise data orchestration layer. You will bring together diverse defense health data to create scalable pipelines, APIs, and data products that improve data access and decision making.

The role requires 3–10 years of data/engineering experience, strong Python/SQL skills, and experience with Spark and Databricks in a production environment.

Qualifications

• Bachelor's degree in a related field is required.
• 3 to 10 years of data/software engineering experience.
• Strong proficiency with Python and SQL.

Responsibilities

• Design, develop, and maintain scalable, production-ready 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 for enterprise reporting and decision-making.
• Build and maintain API-based data integrations across systems.
• Monitor, troubleshoot, and optimize production pipelines for performance and reliability.
• Implement data validation, quality checks, lineage, and governance practices.

Skills

Python

SQL

Spark

Databricks

REST APIs

Git

Education

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

Tools

Databricks

Spark

Job description

Intrepid, an SPA Company, is seeking a Data Engineer to support the Defense Health Agency and build an enterprise data orchestration layer. You will bring together diverse defense health data to create scalable pipelines, APIs, and data products that improve data access and decision making.

The role requires 3–10 years of data/engineering experience, strong Python/SQL skills, and experience with Spark and Databricks in a production environment.