Data Engineer

Tetrad Digital Integrity LLC•Springfield, VA

2d•Onsite

About The Position

Tetrad Digital Integrity (TDI) is seeking Data Engineers to support a National Security mission to position NGA as the enterprise and community GEOINT data provider. The work aims to improve how analysts, customers, and end users access, discover, stage, link, produce, reintegrate, and deliver intelligence. Engineers will guide technical decisions, collaborate across mission and engineering teams, and improve cloud-based data platforms. Multiple positions are available. The role requires full-time onsite support in Springfield, Virginia, and an active TS/SCI security clearance. The Data Engineer will lead the architecture, design, development, and operation of scalable data pipelines and ETL/ELT processes for analytics, reporting, and mission operations. The role includes designing secure data integrations, establishing reliable data processing and storage approaches, strengthening data quality and governance, partnering with stakeholders, resolving production issues, and contributing to Agile delivery. Engineers will also document systems, mentor colleagues, and recommend improvements to data architecture and engineering practices. Employment is restricted to U.S. citizens or lawful permanent residents because TDI does business with the federal government.

Requirements

Active TS/SCI CI Poly clearance.
Bachelor’s degree in computer science, information systems, engineering, mathematics, or a related field plus 5–8 years of relevant experience; or a related master’s degree plus at least 3 years of relevant experience.
Ability to design, write, review, and support production code in Python, Scala, Java, or a comparable language, using Git, automated testing, and CI/CD practices.
Strong experience with ETL/ELT architecture, data modeling, data quality, and secure API integrations, including troubleshooting authentication and data exchange issues.
Strong engineering judgment and experience selecting data architectures and patterns for complex, evolving datasets.
Experience delivering and operating scalable production data pipelines across structured, semi-structured, or unstructured data.
Experience designing API connectors and ingestion patterns that integrate enterprise applications and external services.
Ability to own complex data engineering work from requirements and design through production operation, improving reliability and data quality.
Experience leading technical collaboration with analytics, data science, product, security, or mission teams and mentoring other engineers.
Clear written and verbal communication skills, including the ability to explain technical decisions and tradeoffs.
Must be a U.S. citizen or lawful permanent resident.

Nice To Haves

Knowledge of metadata standards.
Experience with search tools, search-results organization, and the implications of metadata organization, indexing, and catalog content.
Experience in GIS-related applications.

Responsibilities

Lead the architecture, design, development, and operation of scalable data pipelines and ETL/ELT processes for analytics, reporting, and mission operations.
Design and oversee API connectors and integrations that securely ingest data from internal platforms and external services.
Set engineering approaches for data processing, storage, and database performance to meet scale, reliability, and latency needs.
Establish and improve data quality, validation, monitoring, and governance practices across production data products.
Partner with mission stakeholders, analysts, architects, and product teams to define requirements and guide technical solutions.
Lead diagnosis and resolution of complex data integration, pipeline, and production performance incidents.
Coordinate with security and compliance teams to build required protections and controls into data systems.
Maintain architecture and operational documentation, and mentor engineers through design discussions and code reviews.
Contribute to Agile planning and delivery, helping the team break down work and manage technical dependencies.
Evaluate platform and tooling choices, explain tradeoffs, and recommend improvements to data architecture and engineering practices.