IC Data Engineer

The Mitre Corporation•McLean, VA

15h•$155,200 - $232,800•Onsite

About The Position

MITRE's National Intelligence Division is seeking an Intelligence Community (IC) Data Engineer at the Lead career step to provide senior-level data engineering, enterprise data architecture, and integration expertise to an IC sponsor. This full-time role will be embedded on-site with the sponsor in McLean VA and will help design, assess, and modernize enterprise data capabilities that enable mission operations, analytics, and AI. Key activities include defining data architectures and integration patterns; developing technical requirements and interface specifications; assessing cloud and hybrid data platforms and pipelines; improving data quality, metadata, lineage, and interoperability; and developing implementation roadmaps for enterprise data modernization. The successful candidate will bring deep understanding of Intelligence Community missions and modern data engineering practices across cloud and hybrid environments. This individual will be expected to operate with exceptional judgment, discretion, and independence; translate mission needs into actionable data architectures and engineering requirements; assess technical tradeoffs and integration risks; and communicate clear recommendations to senior government leaders and technical teams.

Requirements

Typically requires a minimum of 8 years of related experience with a Bachelor’s degree; or 6 years with a Master’s degree; or 3 years with a PhD; or an equivalent combination of education and relevant experience.
Bachelor's degree in Computer Science, Computer Engineering, Data Science, Data Engineering, Information Systems, Systems Engineering, Mathematics, or a related technical field, or equivalent combination of education and experience.
Demonstrated experience leading technical tasks or small multidisciplinary engineering teams, with hands-on experience designing, implementing, or modernizing enterprise data architectures, data integration solutions, and production data pipelines.
Strong proficiency with SQL and experience using at least one modern programming language, such as Python, Java, or Scala, for data processing, automation, or systems integration.
Experience with data integration methods and technologies such as ETL/ELT, APIs, batch and streaming processing, data modeling, schema design, and interface development.
Experience with cloud or hybrid data platforms, distributed data processing, scalable storage and compute, and modern warehouse, lake, or lakehouse architecture patterns.
Strong understanding of metadata and cataloging, data lineage, data quality and observability, data lifecycle management, interoperability standards, and enterprise information sharing.
Ability to assess data platform scalability, performance, resilience, security, and cost tradeoffs and to identify and resolve complex integration risks and technical dependencies.
Experience developing technical requirements, data and system architecture artifacts, interface documentation, implementation plans, and test or verification approaches.
Strong understanding of Intelligence Community missions, organizations, data-sharing environments, interoperability challenges, security considerations, and enterprise coordination mechanisms.
Demonstrated ability to operate effectively in a complex, classified, matrixed, and fast-paced sponsor environment.
Exceptional written and verbal communication skills, including the ability to translate complex data engineering issues into clear recommendations and brief senior executives or national-level decision makers.
Must have an active Top Secret/SCI w/Poly U.S Government issued Security Clearance. Per the U.S. Government’s eligibility requirements, you must be a U.S Citizen to be considered for a security clearance.
This position has an on-site requirement of 5 days a week on-site.

Nice To Haves

Prior experience supporting data engineering, enterprise data architecture, or data integration efforts within the Intelligence Community or broader national security enterprise.
Advanced degree in Computer Science, Computer Engineering, Data Science, Data Engineering, Information Systems, Artificial Intelligence, or a related technical discipline.
Experience with modern enterprise integration approaches such as data mesh, data fabric, API-centric architectures, event-driven architectures, or federated data services.
Experience applying DevSecOps or DataOps practices, including automated testing, CI/CD, infrastructure as code, containerized data services, and repeatable deployment patterns.
Experience developing or applying structured technology evaluation frameworks, metrics, benchmarks, and scoring methodologies for data platforms or integration technologies.
Experience enabling AI/ML use cases through data engineering, including scalable data preparation, feature or vector data flows, model-data integration, or AI-ready data architectures.
Experience modernizing legacy data environments, migrating data workloads to cloud or hybrid platforms, and managing technical transition risk.
Ability to build trust quickly with senior sponsors and operate with discretion, sound judgment, and minimal direction.
Experience working across multiple IC elements and technical organizations to coordinate enterprise data strategy, architecture, and implementation activities.

Responsibilities

Serve as a lead data engineer and technical advisor supporting sponsor priorities, enterprise data modernization, architecture decisions, and executive decision-making.
Partner with IC sponsors, mission owners, architects, developers, cybersecurity teams, analysts, and other stakeholders to translate mission needs into implementable data engineering requirements, technical designs, and integration plans.
Architect and assess enterprise data platforms and end-to-end pipelines for data ingestion, transformation, storage, distribution, and access across batch and streaming workflows, with attention to scalability, performance, resilience, security, and cost.
Develop and assess data models, schemas, APIs, interface specifications, data contracts, exchange formats, and integration patterns, including ETL/ELT, event-driven integration, messaging, and federated or virtualized data access.
Define and evaluate engineering approaches for metadata and cataloging, data lineage, data quality and observability, master and reference data, lifecycle management, access controls, and interoperability across cloud, hybrid, legacy, and modern data environments.
Develop prototypes, proofs of concept, benchmarks, test strategies, and technical trade studies to validate integration approaches, improve data readiness for analytics and AI, assess capability gaps and dependencies, and reduce implementation risk.
Develop and refine data architecture documentation, engineering requirements, interface specifications, implementation plans, technical assessments, and executive briefings; coordinate across government, MITRE, and contractor teams; and mentor staff supporting related data engineering efforts.