About this opportunity
Peraton lists this Databricks Data Engineer opportunity in workfromhome, Maryland. Review the employer’s description below for duties, qualifications and application requirements.
Job description
Required Qualifications:
5 years work experience with BS/BA; 3 years with MS/MA
US Citizenship
Active DoD Secret clearance
2 years working on the Databricks platform
Strong proficiency in PySpark, Spark SQL, and Python for large-scale data processing and pipeline development
Hands-on experience with Unity Catalog administration, including metastore management, access policies, and data lineage
Experience with pipeline orchestration tools (Airflow, Databricks Workflows)
Experience with Delta Lake, schema evolution, time travel, and optimization techniques
Experience integrating heterogeneous enterprise systems, including legacy/custom integrations
Familiarity with cloud platforms (Azure/AWS) and infrastructure-as-code practices
Understanding of data governance principles, compliance frameworks, and CUI/PII handling practices
Familiarity with financial, HR, CRM, or ITSM data structures
Git/CI-CD pipeline experience
DoD 8570 certification
Preferred Qualifications:
Databricks Certified Data Engineer Associate or Professional certification
The Databricks Data Engineer will be responsible for the hands-on build-out of a Government-owned Databricks workspace and the data ingestion/integration work needed to consolidate agency business system data. This includes designing and implementing data pipelines from core enterprise systems, implementing Unity Catalog for data governance, configuring MLflow for machine learning workflows, and developing comprehensive documentation to support sustainment beyond the pilot.
This role is 100% Remote.
Other Responsibilities Include:
Build out and configure a dedicated Government workspace within the existing Databricks environment
Design and implement data ingestion pipelines from core agency business systems including financial, HR, CRM, and ITSM systems
Leverage native/built-in connectors where source systems support them; design custom integration approaches for legacy systems
Normalize and prepare ingested data within Databricks for consumption by downstream visualization/reporting tools
Implement Databricks Unity Catalog for centralized data governance, metadata management, active auditing, and end-to-end lineage tracking
Review current configuration, assess security controls for CUI/PII/PHI/financial data, and implement improvements
Develop comprehensive "as-built" documentation including physical/logical architecture diagrams, automated data dictionaries, and SOPs
Document data sources, integration methods, and data lake architecture decisions to support sustainment
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Who can apply
Review the original listing for work authorization, qualifications and employer requirements.