Principal Data Engineer

Koniag Government Services•Washington, DC

1d•Onsite

About The Position

Koniag Professional Services (KPS) is seeking a high-performing SETA Principal Data Engineer / Data Architect to support mission-critical artificial intelligence, data analytics, and digital modernization initiatives within the Department of War. This individual will serve as a senior technical leader responsible for designing, engineering, integrating, and optimizing enterprise-scale data environments that enable advanced analytics, machine learning, artificial intelligence, and operational decision-making. The ideal candidate combines deep hands-on data engineering expertise with enterprise architecture experience and the ability to operate effectively with senior government, technical, acquisition, and mission stakeholders. This is not a traditional data-pipeline development position. The successful candidate will help define the technical direction for complex DoW data ecosystems, solve high-impact integration and interoperability problems, and translate mission requirements into scalable, secure, production-ready data solutions.

Requirements

Current TS/SCI clearance
Bachelor’s degree in Computer Science, Data Science, Engineering, Information Systems, Mathematics, or a closely related technical discipline.
Approximately 10–15+ years of progressively responsible technical experience, including substantial experience designing and delivering enterprise-scale data solutions.
Demonstrated experience serving as a senior, principal, lead, or architect-level data engineer.
Expert-level proficiency with Python and SQL.
Extensive experience with modern data engineering technologies such as Apache Spark, Databricks, Kafka, Airflow, or comparable platforms.
Significant experience architecting cloud-based data solutions within AWS, Azure, or comparable enterprise cloud environments.
Strong understanding of data lake, lakehouse, warehouse, data mesh, data fabric, and distributed data-processing architectures.
Experience developing production-grade ETL/ELT pipelines and integrating large numbers of heterogeneous data sources.
Strong knowledge of APIs, microservices, data integration patterns, containerization, CI/CD, infrastructure automation, and DevSecOps principles.
Experience designing data architectures that support AI/ML workloads and production analytics.
Demonstrated ability to work with complex mission requirements where technical, cybersecurity, policy, acquisition, and operational considerations intersect.
Ability to communicate sophisticated technical concepts clearly to senior executives and nontechnical stakeholders.
Demonstrated ability to independently solve ambiguous, enterprise-level technical problems.

Nice To Haves

Experience supporting OSW, CDAO, military departments, defense agencies, Combatant Commands, or other DoW enterprise organizations.
Experience with DoW enterprise data and analytics environments such as Advana/War Data Platform or comparable platforms.
Experience with Databricks and large-scale Spark implementations.
Experience engineering data platforms supporting generative AI, large language models, retrieval-augmented generation, vector databases, or knowledge graphs.
Experience operating across SIPRNet, JWICS, or other classified computing environments.
Knowledge of DoW data strategy, zero-trust principles, data tagging, identity/access management, and cross-domain data challenges.
Experience with Palantir Foundry, Snowflake, PostgreSQL, Elasticsearch/OpenSearch, Neo4j, or similar technologies.
Experience with Kubernetes, Docker, Terraform, GitLab/GitHub, and automated deployment pipelines.
Familiarity with MLOps and the operational deployment of AI/ML models.
Experience supporting technical evaluations, source selections, acquisition strategies, statements of work, performance work statements, or government technical requirements.
Master’s degree in a relevant technical discipline.
Relevant certifications such as AWS Solutions Architect Professional, Azure Solutions Architect Expert, Databricks Data Engineer Professional, CISSP, or comparable advanced technical credentials.

Responsibilities

Serve as a senior AI2 technical authority in support of government for enterprise data engineering, data architecture, integration, and AI-enabling infrastructure.
Design and implement highly scalable data architectures supporting structured, semi-structured, and unstructured data across classified and unclassified environments.
Architect and develop resilient ETL/ELT pipelines, data products, APIs, streaming solutions, and automated data-processing workflows.
Establish architectures capable of supporting machine learning, generative AI, advanced analytics, decision-support applications, and large-scale data exploitation.
Design solutions for cloud, hybrid-cloud, and government-hosted environments while accounting for DoW cybersecurity, access-control, compliance, and data-governance requirements.
Lead integration of disparate DoW, Service, intelligence, acquisition, financial, operational, and business data sources.
Develop and implement data models, common data standards, metadata strategies, lineage, data quality controls, and data-governance mechanisms.
Optimize large-scale data platforms for performance, reliability, cost, scalability, and availability.
Work closely with data scientists from multiple sectors and backgrounds, AI/ML engineers, software engineers, cybersecurity personnel, cloud architects, and mission stakeholders to transition prototypes into operational capabilities.
Provide technical leadership for architecture reviews, design decisions, engineering standards, and technology-selection activities.
Evaluate emerging technologies and recommend modernization opportunities involving AI, data fabrics, data meshes, knowledge graphs, vector databases, lakehouse architectures, and distributed computing.
Diagnose and resolve highly complex technical issues involving data availability, latency, interoperability, security, and system performance.
Develop technical roadmaps and communicate architectural alternatives, technical risks, tradeoffs, and recommendations to senior government and contractor leadership.
Mentor engineers and establish engineering practices that improve quality, repeatability, automation, documentation, and delivery velocity.
Support technical planning, requirements development, acquisition activities, technical evaluations, and government decision-making as required.