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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Automation Data Integration Engineer - **Company:** Ultraviolet Cyber - **Location:** Washington, DC, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Airflow, Amazon Web Services, Automation of Tests, Microsoft Azure, Cloud Computing, Cloud Computing Security, Computer Programming, Databases, Continuous Integration, Data Cleansing, Data Governance, Data Integration, Data Integrity, Extract Transform Load (ETL), Data Warehousing, Github, Python (Programming Language), Meta-Data Management, SQL Databases, Workflow Management Systems, Scripting, Google Cloud, Reliability of Systems, Gitlab, Cloudformation, Infrastructure Automation Frameworks, Terraform, Data Pipelines, Jenkins - **Published:** August 26, 2026 - **Apply:** https://www.clearancejobs.com/jobs/9117160/automation-data-integration-engineer ## About the Role U.S. Citizenship required with Active Secret Security Clearance - DHS EOD suitability or Current DHS or DHS EOD preferred - BS/BA + 8 years of applicable experience in data integration and automation - Demonstrated expertise in Apache Airflow, Prefect, or similar orchestration tools - Experience with ETL/ELT processes and automation tools - Knowledge of data quality frameworks and observability systems - Experience with APIs, databases, and data warehouse/lake technologies - Strong programming skills in Python, SQL, or similar languages Desired: - Previous DHS or DoD experience with classified systems - Experience with Infrastructure as Code (IaC) tools (Terraform, CloudFormation) - Knowledge of data catalog and metadata management solutions - Experience with CI/CD pipelines (Jenkins, GitLab, GitHub Actions) - Familiarity with cloud platforms (AWS, Azure, GCP) - Cloud security experience with AWS, Azure, or other platforms ## Description UltraViolet Cyber (UVC) is searching for an Automation Data Integration Engineer to support DHS in Washington, D.C. As an Automation Data Integration Engineer, you will play a crucial role in enhancing the efficiency and effectiveness of DHS's cybersecurity operations. You will work in a dynamic environment, collaborating with cybersecurity professionals, data analysts, and system owners to design and implement automated solutions that reduce manual effort and improve risk visibility. Your efforts will directly contribute to safeguarding DHS's mission-critical systems and data through advanced automation and integration. The Automation Data Integration Engineer will be responsible for maintaining cybersecurity automations, connectors, data pipelines, and dashboards that reduce manual effort and improve risk visibility across tools and workflows. This position requires creating and managing end-to-end pipelines ingesting data from diverse sources (APIs, databases, IoT) and delivering data to data warehouses or lakes. The Automation Data Integration Engineer will automate extraction, transformation, and loading processes to ensure data is clean, standardized, and ready for analysis. This role is critical for implementing automated tests, anomaly detection, and real-time alerts to validate data integrity. What You'll Do: Design and maintain cybersecurity automations, connectors, data pipelines, and dashboards that reduce manual effort and improve risk visibility across tools and workflows. Perform Data Pipeline Automation by creating and managing end-to-end pipelines that ingest data from diverse sources (APIs, databases, IoT) and deliver it to data warehouses or lakes. Perform ETL/ELT Optimization by automating the extraction, transformation, and loading process to ensure data is clean, standardized, and ready for analysis. Perform Data Quality & Observability by implementing automated tests, anomaly detection, and real-time alerts to validate data integrity and identify issues. Perform Workflow Orchestration by scheduling and managing dependencies for data jobs using tools like Apache Airflow or Prefect. Perform System Reliability & Maintenance by building self-healing pipelines that can automatically retry failed tasks and scale resources based on demand. Perform Data Cleansing & Validation by running automated scripts to detect data anomalies, impute missing values, or mask sensitive information. 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