Senior Data Engineer

Salary: $148,750 - 201,250 per year Requirements: Active TS/SCI clearance. At least 7 years of experience in data engineering, data platform engineering, or a closely related field. Strong SQL and Python skills for data processing, automation, and integration. Hands-on experience building and operating production data pipelines and ETL/ELT workflows. Experience with relational databases and modern storage patterns for structured and unstructured data. Experience integrating data through REST APIs, message or event systems, or other distributed integration patterns. Working knowledge of data modeling, schema design, data quality, lineage, metadata, and governance. Experience in cloud-native or containerized environments and collaborating with Kubernetes or platform engineering teams. Ability to troubleshoot data issues across infrastructure, applications, APIs, storage, networking, and access controls. Ability to work independently, learn new technologies quickly, and turn ambiguous mission needs into working data solutions. Preferred: Experience supporting AI/ML workloads such as RAG, embeddings, vector search, model context, or agentic applications. Preferred: Experience with vector, graph, or document databases; knowledge graphs; or semantic search technologies. Preferred: Experience with Oracle or comparable enterprise data platforms supporting relational, JSON/document, vector, graph, and analytics workloads. Preferred: Experience designing data products or platforms for high-scale, low-latency, or mission-critical workloads. Preferred: Experience with workflow and orchestration tools such as Airflow, Dagster, or Argo Workflows, and streaming or event platforms such as Kafka, Redpanda, or NATS. Preferred: Experience deploying data services with Kubernetes, Helm, GitOps, Infrastructure as Code, and automated CI/CD pipelines. Preferred: Experience supporting data and AI systems in Secret or TS/SCI environments, across multiple security domains, or in disconnected and air-gapped environments. Preferred: Familiarity with NIST 800-53, RMF/ATO processes, zero-trust architectures, and data security and compliance practices. Preferred: Experience with enterprise search, RAG pipelines, data catalogs, data contracts, or knowledge-graph architectures. Preferred: Familiarity with UDS, Zarf, Pepr, Iron Bank, or similar secure software delivery technologies. Preferred: Experience on multidisciplinary teams involving government engineers, forward-deployed engineers, OEM professional services teams, and multiple technology vendors. Preferred: Demonstrated ability to turn fragmented or difficult data environments into reliable, reusable foundations for mission applications and AI. Responsibilities: Design, build, and maintain dependable pipelines that ingest, transform, validate, and deliver data for AI models, agents, and mission applications. Integrate structured and unstructured sources into enterprise data services, including relational, document, vector, and graph technologies. Partner with AI and agent engineers to make data discoverable and useful for RAG, semantic search, model context, and tool-enabled agent workflows. Develop reusable ingestion and transformation patterns using Python, SQL, APIs, event-driven architectures, and workflow or orchestration technologies. Establish data quality, validation, provenance, lineage, and metadata practices so users and AI systems can trust the information they consume. Design data access patterns that enforce identity, authorization, classification, and security boundaries while reducing unnecessary friction for developers and mission users. Work with platform engineers to operationalize data services in Kubernetes or OpenShift environments and integrate them into CI/CD and GitOps workflows. Integrate enterprise data capabilities—including relational stores, JSON/document data, vector search, graph data, advanced analytics, and RAG services—into the broader platform architecture. Troubleshoot end-to-end data issues across source systems, pipelines, storage, APIs, identity, networking, platform services, and AI applications. Work directly with government engineers and commercial technology partners to resolve data dependencies and remove barriers to mission delivery. Create documentation, data contracts, schemas, architecture decision records, runbooks, and operational standards to make effective patterns repeatable across environments. Identify recurring data engineering challenges that can be standardized, automated, or productized instead of repeatedly addressed through manual engineering. Adapt to an evolving environment in which data sources, architectures, security needs, and mission priorities change. Technologies: AI Airflow Business Intelligence CI/CD Cloud ETL Embedded GitOps Helm Support JSON Kafka Kubernetes NATS OpenShift Oracle Python RAG REST SQL Security AWS Azure GCP Kustomize Pulumi Terraform More: We are partnering with hackajob to connect professionals with Defense Unicorns for a senior Data Engineer role embedded with a government team and its technology partners. The role builds secure, trusted data foundations that help AI and agentic capabilities move from prototype to operational use; it is not a traditional analytics or business intelligence position. We deliver secure continuous software integration and delivery solutions, bringing together security pipelines, testing, and deployment automation for mission-focused customers. Our team includes innovators, software engineers, and veterans with extensive federal technology experience, and we support defense and civil agencies as well as public and private enterprise customers. Our work includes Kubernetes, cloud environments, infrastructure as code, containers, GitOps, continuous delivery, automation, and open-source cloud-native technologies. The position is listed as remote within the United States, with the candidate expected to be local to the National Capital Region and able to work onsite at a government facility in Springfield, Virginia as mission needs increase. The listed salary range is $148,750–$201,250 USD, with compensation based on experience and national benchmarking. Our benefits include company-paid medical, dental, and vision premiums, a health savings account, life and disability insurance, a 401(k), stock options, and a home-office budget. We also offer flexible time off, federal holidays, additional Thanksgiving and Christmas/New Years leave, paid parental leave, and reimbursement for approved training and subscriptions. last updated 41 week of 2026