Cloud Data Engineer - Clearance Required

Phoenix Ops Group is seeking qualified candidates for mission-oriented cloud, data, and AI-enabling technology work. Multiple levels may be considered based on experience, including mid-level and senior candidates. We are seeking a hands‑on Cloud Data Engineer to support cloud‑based data pipelines, APIs, data services, and modern data platforms.

This role is focused on helping integrate data sources, services, pipelines, and applications into secure, operational cloud environments that support advanced analytics, automation, and AI‑enabled mission capabilities. Candidates should be comfortable working across data engineering, ETL, API integration, software development, distributed services, and cloud‑based platform teams.

Experience with AWS and Databricks is highly desired.

• Design, develop, test, and support cloud‑based data engineering capabilities.
• Build, maintain, and troubleshoot ETL pipelines and data workflows.
• Integrate data sources, APIs, applications, distributed services, and cloud services.
• Develop and support data processing pipelines, database applications, and data services.
• Support data movement, transformation, validation, testing, and operational readiness activities.
• Develop or integrate RESTful APIs, services, and data‑driven applications where needed.
• Work with cloud platform, software engineering, and Dev Sec Ops  teams to support deployment of integrated capabilities.
• Use Git‑based source control and CI/CD practices, including tools such as Git Lab CI/CD, Jenkins, Git Hub Actions, or comparable platforms.
• Participate in Agile development activities, code reviews, testing, debugging, and technical documentation.
• Assist with deployment, configuration, maintenance, and troubleshooting in Linux and cloud‑based environments.
• Document data flows, interfaces, system dependencies, code comments, integration approaches, and technical decisions.
• Collaborate with mission, engineering, data, security, technical, and user stakeholders to support practical implementation needs.
• Apply secure coding, automated testing, and engineering best practices throughout the development lifecycle.
• Support secure software and data practices, including safeguarding sensitive, proprietary, CUI, or classified information where applicable.
• Operate effectively in secure, government, defense, or mission‑oriented environments.

• Hands‑on experience in data engineering, data integration, cloud data development, or software engineering.
• Experience developing ETL pipelines, data workflows, or data processing capabilities.
• Experience integrating APIs, applications, services, databases, or data sources.
• Experience developing software in modern languages such as Python, Java, or JavaScript/Type Script.
• Familiarity with RESTful APIs, distributed applications, or cloud‑based software environments.
• Familiarity with cloud‑based data platforms and cloud‑native services.
• Experience developing, testing, or troubleshooting in Linux‑based development or operational environments.
• Familiarity with Git‑based source code management and collaboration tools.
  
  Familiarity with Agile development, code reviews, testing, debugging, and collaborative engineering practices.
• Familiarity with secure coding, automated testing, and software engineering best practices.
• Ability to troubleshoot data pipeline, data integration, API, database, software, and interface issues.
• Strong analytical, problem‑solving, verbal, and written communication skills.
• Ability to work collaboratively within engineering teams and adapt to evolving technical requirements.
• Ability to support secure or mission‑oriented environments.

• Experience with AWS cloud services.
• Experience with Databricks or comparable cloud data platforms.
• Experience with Python, SQL, Spark, Java, or similar data engineering technologies.
• Experience with Git Lab, Git Lab CI/CD, Jenkins, Git Hub Actions, or comparable development and CI/CD platforms.
• Experience working with relational and/or No
  
  SQL databases.
• Experience with unit testing or automated testing frameworks.
• Experience with data modeling, large‑scale data processing, event‑driven architectures, or…