S. Federal Government. Headquartered in McLean, VA, CTEC has over 300 team members working on mission-critical systems and projects for agencies such as the Department of Homeland Security, Internal Revenue Service, and the Office of Personnel Management.

S. citizens daily as they interact with the systems we build.

Requirements

, are enabling this future every day. The Company has experienced rapid growth over the past 3 years and recently received a strategic investment from Main Street Capital Corporation (NYSE: MAIN). S.

Intelligence Community. We are seeking to hire a Data Engineer to our team! Client: CTEC develops and delivers innovative customer-centric technologies and solutions that support the Office of Personnel Management’s (OPM) Health and Insurance business unit and Office of the Chief Information Officer (OCIO).

DevOps & CI/CD Support: Maintain source control and CI/CD pipelines for Databricks and data engineering workflows, supporting automated promotion across environments. Technical Collaboration: Work closely with data architects, solution architects, business analysts, and reporting teams to implement approved data solutions. Operational Support & Troubleshooting: Provide ongoing support for Databricks workflows, resolve pipeline failures, and troubleshoot complex data processing issues.

Mentorship & Knowledge Sharing: Provide guidance to junior data engineers and contribute to documentation and team enablement. Works under minimal supervision with minor guidance from senior personnel. Skills & Work Experience: Professional Experience: 7+ years of experience in data engineering, ETL development, or large-scale data integration environments.

Strong experience designing and developing ETL pipelines and data transformations in Azure Databricks environments. Strong proficiency in SQL and Python, with hands-on experience using PySpark for distributed data processing.

Responsibilities

: Data Pipeline & ETL Development: Design, develop, and maintain scalable ETL pipelines and data workflows to ingest, transform, and integrate data from legacy systems and external sources into modern cloud-based data platforms. Cloud Data Platform & Databricks Implementation: Build, optimize, and maintain data processing solutions using Azure Databricks and lakehouse architectures to support analytical, operational, and reporting use cases. Data Migration Support: Support phased data migration from legacy databases and ETL tools to Azure Databricks environments, including transformation documentation and data mapping.

, bronze, silver, gold layers) in Databricks to support data quality, performance, and downstream reporting needs. Python & PySpark Development: Develop data processing notebooks, workflows, and distributed data transformations using Python and PySpark within Databricks environments. Data Quality & Validation: Develop data validation, reconciliation, and testing processes to ensure data accuracy, completeness, and consistency across data domains.

Data Integration & Interoperability: Integrate Databricks data platforms with analytics and reporting tools to enable business intelligence and operational dashboards.