Build and maintain production-grade batch and streaming data pipelines in Databricks using PySpark and SQL. Develop ingestion frameworks, data models, APIs, data quality checks, monitoring, and Medallion Architecture across bronze, silver, and gold layers. Integrate diverse data sources, optimize Spark workloads, document data lineage and architecture, and collaborate with engineering and federal stakeholders. An active Secret clearance or higher is required.
This is a remote position.
Location: Remote – United States
Security Clearance: Active Secret or higher REQUIRED
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.
- 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.
Requirements
- 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.
- 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.
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