FDE Data Engineer- Space

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 — and you'll be the person who takes UDS Data Capability from "deployable" to "running in production under classification." You will deploy, harden, integrate, and operate our data stack in the mission hero's environment, then carry what you learn back into the product so the next engagement is easier.

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 genuinely calls for it — initial standup, in-SCIF integration work, on-the-ground incident response, training and knowledge transfer — and you'll be on a video call or in a shared chat with that same mission hero the rest of the time. The job is being their engineer, not living at their desk.

You are a data engineer, a data scientist, and a general solutioner. You can take responsibility for the full breadth of a data platform — storage, ingestion, streaming, governance, access, and the Kubernetes machinery underneath all of it — make it work where it has to work, analyze what's running through it, and solve problems in real time for the mission hero. About 20% of your time, you'll also be what makes Defense Unicorns' forward-deployed team itself better — pairing with junior Data Engineer FDEs on hard problems, helping them develop the technical judgment that only comes from doing hard things, and raising the ceiling on what the team can take on.

Responsibilities:

• Deploy and harden 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.
• Own the integration with what they already have — connect UDS Data Capability to whatever's already running: Big Bang, legacy Oracle and SQL Server, flat-file drops, SOAP/REST endpoints, message buses, existing object storage, identity providers (Keycloak, mission-side SSO).
• Build pipelines that move data through classification boundaries — ingestion, transformation, catalog registration, model/dataset packaging via Zarf, cross-domain transit, eventual consistency across DDIL conditions.
• Operate what you deploy — initial day-2 ownership: capacity, performance, backup/restore (Velero), observability (Vector/Loki), incident response, upgrade paths. Hand off to the mission hero's ops team once it's stable.
• Generate accreditation artifacts — STIG evidence, cATO documentation, FIPS validation notes, policy mappings. You produce the evidence the mission hero's ISSM/ISSO needs to actually run this in IL4/IL5.
• Be the voice of the mission hero back to product and engineering — file the issues, write the postmortems, propose the operator improvements, push the platform team on what's actually breaking in the field. Your field experience is the highest-signal input we have.
• Train and transfer — leave the mission hero's team self-sufficient: runbooks, architecture docs, working sessions, knowledge transfer.
• Grow junior Data Engineer FDEs — pair on hard problems, review integration designs before they reach the customer, and help junior engineers build judgment faster than they would alone. You're not managing anyone; you're making the team better.

Travel Expectations: 10%-25%

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. 

Preferred Experience and Qualifications:

• Data engineering breadth

  • Lakehouse & storage — production experience with Apache Iceberg (or Delta/Hudi), object storage (Ceph/S3-compatible), Postgres (including extensions like pgvector), and at least one columnar/OLAP engine (Trino, DuckDB, ClickHouse, Spark SQL).
  • Streaming & integration — Kafka (preferably Strimzi on Kubernetes), Flink or equivalent stream processing, CDC patterns (Debezium), and the ability to bridge legacy systems (Oracle, SQL Server, flat files, SOAP) into modern pipelines.
  • Pipelines