FDE Data Engineer- Space (FedD147)

EMPLOYER IS A CONTRACTOR FOR THE U.S. GOVERNMENT. THIS POSITION WILL REQUIRE U.S. CITIZENSHIP.

Role Description

This is a forward-deployed engineering role. You'll work shoulder-to-shoulder with mission heroes: the users, operators, and program engineers who run real workloads in real DoD environments. You'll help take UDS Data Capability from "deployable" to "running in production under classification." You will deploy, integrate, and operate our data stack in the mission hero's environment alongside senior engineers, then carry what you learn back into the product so the next engagement is easier.

The scope of this work varies by engagement. Some programs require standing up a governed data platform to centralize sensor or operational data from known sources, where the problem is well-defined and the priority is building and shipping. Others involve consolidating data across hundreds of interconnected government systems of record with overlapping schemas, deeply interdependent data flows, and significant architectural complexity. You should be comfortable building independently against defined problems and growing into more ambiguous, complex engagements over time.

Forward-deployed means aligned with the mission hero, not always on a plane. Most of the work is remote. You'll travel to sites when the engagement calls for it: initial standup, integration work, incident response, training and knowledge transfer. The rest of the time, you'll be on a video call or in a shared chat with that same mission hero. The role is defined by the relationship with the mission hero, not by where you sit.

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.

Responsibilities

Deploy and configure UDS Data Capability in the mission hero's environment. Stand up the UDS Store (Iceberg, Rook/Ceph, pgvector, Postgres), wire up UDS Transit for air-gap data movement, configure UDS Govern policies (Pepr/Lula), and integrate UDS Connect (Strimzi/Kafka) where streaming or legacy connectors are required.

Build and support integrations with existing mission systems. Connect UDS Data Capability to legacy databases, flat-file drops, SOAP/REST endpoints, message buses, existing object storage, and identity providers (Keycloak, mission-side SSO). Prior experience integrating with these types of systems is more relevant than experience building on them.

Contribute to mapping complex data landscapes. Some engagements involve hundreds of interconnected systems of record with overlapping schemas and deeply interdependent data flows. You'll help trace how data moves across systems and identify dependencies alongside senior engineers and government stakeholders.

Build pipelines that move data through classification boundaries, including ingestion, transformation, catalog registration, model/dataset packaging via Zarf, cross-domain transit, and eventual consistency across DDIL conditions.

Implement data provenance, lineage, and governance practices. Track where data came from, how it transformed, and who can access it.

Operate what you deploy. Day-2 ownership includes capacity, performance, backup/restore (Velero), observability (Vector/Loki), incident response, and upgrade paths. Hand off to the mission hero's ops team once it's stable.

Generate accreditation artifacts, including STIG evidence, cATO documentation, FIPS validation notes, and policy mappings. You produce the evidence the mission hero's ISSM/ISSO needs to run this in IL4/IL5.

Contribute field feedback back to product and engineering. File issues, write postmortems, and surface what's working and what's breaking in the mission environment.

Support training and knowledge transfer. Contribute to runbooks, architecture docs, and working sessions that leave the mission hero's team self-sufficient.

The listed responsibilities are not exhaustive and additional responsibilities may be assigned based on the evolving needs of the organization. We are seeking a dynamic individual who is able to adapt and take on new responsibilities as they arise.

Required Qualifications

Data engineering breadth

Unstructured data at scale. Experience storing and querying large unstructured datasets using data lake architectures. Spark experience preferred.

Streaming & integration. Experience with stream processing infrastructure (Kafka, Redpanda, Flink, or equivalent) and bridging data from heterogeneous sources into modern 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.

Pipelines & orchestration. Airflow, Dagster, Argo Workflows, or similar. Comfort building, scheduling, monitoring, and recovering production data pipelines.

Data modeling & SQL. Fluent in SQL. Comfortable designing schemas for both analytical and operational workloads.