This position may be filled prior to the posted deadline. Interested candidates are encouraged to apply as soon as possible. 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. Key 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. Desired Qualifications: Required: - 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. Preferred: - 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.