Databricks Data Engineer

Our client, a growing technology services organization supporting federal government programs, is seeking an experienced Databricks Data Engineer to support a federal technology initiative.

This is a remote opportunity available to U.S. citizens residing in the United States. Candidates must currently hold an ACTIVE Secret security clearance or higher. Candidates without an active Secret-level clearance cannot be considered.

The ideal candidate will bring strong hands-on data engineering experience within Databricks, including development of production-grade batch and streaming pipelines, PySpark and SQL transformations, data modeling, ingestion frameworks, and modern data architecture practices. Responsibilities

• Design, build, and maintain batch and streaming data pipelines using PySpark, SQL, Databricks Workflows, and Delta Live Tables.
• Implement Medallion Architecture across bronze, silver, and gold data layers to support data quality, transformation, and consumption.
• Develop scalable ingestion frameworks for structured, semi-structured, and unstructured data.
• Integrate data from files, databases, APIs, and streaming sources such as Kafka, Kinesis, and Databricks Auto Loader.
• Design dimensional and domain-specific data models supporting analytics and downstream applications.
• Build and consume APIs for integration with downstream systems.
• Optimize Spark workloads for performance and cost through partitioning, caching, cluster sizing, and related techniques.
• Develop and implement data quality checks, validation processes, and pipeline monitoring.
• Maintain documentation covering data flows, lineage, architecture, and integration points.
• Collaborate with engineering, platform, architecture, and federal program stakeholders throughout the development lifecycle.

• Active Secret security clearance or higher is required.
• 5+ years of professional data engineering experience.
• 2+ years of hands-on Databricks experience.
• Strong production-level experience with PySpark and SQL.
• Experience developing both batch and streaming data pipelines.
• Strong understanding of data modeling and modern data architecture principles.
• Proficiency with Python.
• Experience working within Git-based development and CI/CD environments.
• Familiarity with Databricks Unity Catalog, including catalogs, schemas, and permissions from a data engineering perspective.
• Experience integrating data from multiple source types, including databases, APIs, files, and streaming platforms.

Preferred Qualifications

• Experience with Databricks Delta Live Tables.
• Experience with Databricks Workflows.
• Experience with Kafka, Kinesis, or similar streaming technologies.
• Experience working within federal, regulated, or security-sensitive environments.
• Bachelor’s degree in Computer Science, Engineering, Information Technology, or a related discipline.