Data Engineer

We are looking for an experienced Data Engineer for a permanent opportunity in Northern, Virginia. In this role, you will design reliable data solutions that support analytics and business decision-making while helping improve the overall data environment. The ideal candidate brings strong technical depth in modern data engineering practices and enjoys building scalable systems from the ground up. Responsibilities: • Design, develop, and maintain robust data pipelines that collect, transform, and deliver high-quality data for reporting and analysis. • Create and optimize data models that support business intelligence, analytics, and operational use cases across the organization. • Build and manage ETL workflows using Python and related technologies to ensure efficient movement of data between systems. • Work with large-scale data processing tools such as Apache Spark and Hadoop to handle complex and high-volume datasets. • Integrate streaming and batch data sources using technologies such as Apache Kafka to support timely and reliable data availability. • Collaborate with analysts, engineers, and business stakeholders to translate data needs into scalable engineering solutions. • Improve data warehouse performance, structure, and reliability to support accurate and accessible enterprise data. • Use dbt and other modern data transformation practices to organize, test, and document datasets effectively.

• At least 5 years of experience in data engineering, data analytics, or a closely related field.
• Strong hands-on experience building and maintaining data solutions with Python.
• Practical expertise with dbt for data transformation, testing, and modeling.
• Experience working with enterprise data warehouse platforms and dimensional or analytical data models.
• Proficiency with Apache Spark for distributed data processing.
• Familiarity with big data technologies such as Apache Hadoop and event-driven tools such as Apache Kafka.
• Solid understanding of ETL principles, data integration patterns, and data quality best practices.