FDE Data Engineer (Space)

Join our team as a forward-deployed FDE Data Engineer (Space) where you'll work closely with mission heroes in real DoD environments. You'll be responsible for deploying, integrating, and operating our data stack, building pipelines, implementing data governance practices, and generating accreditation artifacts. This role requires a strong background in data engineering, experience with open-source tools, and the ability to work independently in complex environments. Enjoy benefits such as unlimited paid time off, medical/dental/vision coverage, and company stock options.

• Déployer, intégrer et faire fonctionner la capacité de données UDS dans l'environnement du héros de la mission, en collaboration avec des ingénieurs seniors.
• Contribuer à la cartographie de paysages de données complexes, en aidant à tracer comment les données se déplacent à travers les systèmes et à identifier les dépendances.
• Construire des pipelines qui déplacent les données à travers les frontières de classification, y compris l'ingestion, la transformation, l'enregistrement dans le catalogue, l'emballage du modèle/dataset.

• You should be comfortable building independently against defined problems and growing into more ambiguous, complex engagements over time
• You are a data engineer who can build across the breadth of a data platform (storage, ingestion, streaming, governance, and access) and deliver working solutions in constrained, mission-critical environments
• We are seeking a dynamic individual who is able to adapt and take on new responsibilities as they arise
• Pipelines & orchestration. Airflow, Dagster, Argo Workflows, or similar. Comfort building, scheduling, monitoring, and recovering production data pipelines
• Data warehousing. Open-source data warehousing platform experience. These environments do not support proprietary platforms, so you need to be comfortable building without them
• Streaming & integration. Experience with stream processing infrastructure (Kafka, Redpanda, Flink, or equivalent) and bridging data from heterogeneous sources into modern pipelines
• Unstructured data at scale. Experience storing and querying large unstructured datasets using data lake architectures. Spark experience preferred
• Open-source orientation. You are comfortable building on open-source tooling and contributing back to it
• Data modeling & SQL. Fluent in SQL. Comfortable designing schemas for both analytical and operational workloads
• Comfort with periodic on-site work, sometimes for days at a stretch, and equal comfort working remotely
• Comfort working directly with mission heroes and government stakeholders. Clear communication with both technical and non-technical audiences
• Self-direction. You will encounter environments and problems that are not yet documented and will need to work through them independently
• Bias toward delivery. Preference for shipping a working integration over perfecting a design that hasn't met a real workload
• U.S. citizenship and the ability to obtain and maintain a DoD security clearance. Clearance sponsorship available for the right candidate
• You do not need to meet every preferred qualification. You do need to be credible across the core data engineering requirements
• DoD or defense program experience
• Data provenance, lineage, and governance. Experience with lineage tracking, data catalogs, provenance systems, or governance frameworks. Depth here will be weighted heavily
• Active Secret clearance (or higher)
• Change Data Capture: Debezium or similar CDC patterns
• Lakehouse & storage: Apache Iceberg (or Delta/Hudi), object storage (Ceph/S3-compatible), Postgres (including extensions like pgvector), columnar/OLAP engines (Trino, DuckDB, ClickHouse, Spark SQL)
• Governance, catalog & access: REST catalogs (Iceberg REST, Polaris/Gravitino/Nessie family), ABAC/RBAC patterns, OIDC/OAuth, lineage and audit
• Linux fundamentals, container runtime behavior, networking, TLS, secrets management
• Kubernetes awareness. Deep K8s expertise is not required; general familiarity with deployments, operators, and how applications run on Kubernetes is valuable
• IaC (Terraform, Pulumi, or similar) and GitOps patterns (Flux, ArgoCD)
• Familiarity with the CNCF ecosystem, including the distinction between foundation projects and single-vendor projects
• AI/ML awareness. General understanding of how data infrastructure supports model training, versioning, provenance, and AI operations
• Familiarity with Air Force or Space Force systems of record (e.g., MILPDS, ARMS) and how data flows between them