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Senior Data Engineer

Govcio LLCFairfax, Virginia🇺🇸United StatesPosted 10 Sept 2026

Quick Overview

Seniority
Mid Senior
Employment type
Full Time
Work mode
Hybrid
Location
Fairfax, Virginia, United States
GCPSQLScalaAWSETLAzurePython

Job Description

GovCIO LLC seeks a Senior Data Engineer to design and optimize secure, scalable data solutions that modernize federal IT. In this mission-driven role, you'll architect and build cloud-based data pipelines, warehouses, and lakehouse platforms that power analytics and digital services for government agencies. You'll work with modern tech across AWS, Azure, or GCP, integrating diverse data sources, ensuring data quality, and implementing strong governance and security in regulated environments. Collaborating with engineers, analysts, and mission stakeholders, you'll lead complex projects, mentor teammates, and drive best practices-while growing your skills through training, mentorship, and impactful public-sector work.

Responsibilities

  • Design, build, and optimize scalable data pipelines and ETL processes for federal IT systems
  • Develop and maintain data warehouses and lakehouse architectures in cloud environments
  • Implement data models, schemas, and performance tuning for analytics and reporting
  • Ensure data quality, reliability, security, and governance across multiple data sources
  • Collaborate with data scientists, analysts, and application teams to deliver mission-focused solutions
  • Automate data workflows, monitoring, and deployment using CI/CD and infrastructure-as-code
  • Support migration of legacy government data systems to modern cloud-native platforms
  • Troubleshoot complex data issues and drive root-cause analysis and remediation
  • Contribute to technical standards, best practices, and documentation for data engineering
  • Mentor junior engineers and participate in code reviews and architecture discussions

Required Skills

  • SQL and advanced query optimization
  • Python or Scala for data engineering
  • ETL/ELT pipeline design and implementation
  • Cloud data platforms (AWS, Azure, or GCP)
  • Data warehousing and dimensional modeling
  • Distributed data processing (Spark or similar)
  • Data lake/lakehouse architectures
  • CI/CD and infrastructure-as-code tools
  • Data governance, security, and compliance
  • Monitoring, logging, and performance tuning

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