About Gallatin

At Gallatin, we are rebuilding logistics infrastructure for the national security missions of the United States and allied partners. We build AI systems that determine how logistics decisions are made — not just how they're executed. From factory to foxhole, we operate at the layer where data becomes decisions, and decisions make the advantage.

We believe that you won't change the world by phoning it in and as such we work in-office in one of our core engineering locations: El Segundo, CA; Austin, TX; Washington, DC; San Francisco, CA.

About the Role

Logistics decisions are only as good as the data underneath them, and in our world that data is fragmented across legacy systems of record, spreadsheets, slide decks, message traffic, and sensor feeds that were never designed to talk to each other. The Data Platform team connects those sources, cleans and models the data, and serves it to Navigator and our AI agents.

We're hiring a Senior Data Engineer to build that platform. You'll design and ship containerized services that create and manage external data connections, run an open lakehouse, and power the transformations that take raw source data to curated, semantically modeled entities. You'll also build the tools on top: fullstack applications that let logistics SMEs and analysts connect sources, map fields, and test pipeline changes on their own.

The platform runs everywhere Navigator runs: commercial and government cloud, single-tenant high-side enclaves, and air-gapped appliances at forward sites. That means open-source, Kubernetes-native components, hardened images, and data movement that tolerates intermittent and disconnected networks.

Success means a mission partner can point us at a new system of record and have validated, lineage-tracked, query-ready data flowing into Navigator within days.

About You

You start with the catalog: where data came from, what it means, and how much to trust it. You've integrated data from systems that disagree with each other and built the models and mappings that reconcile them.

You're a data engineer with strong software engineering skills, comfortable building a React or Vue front end for the pipeline tools you design. You bias toward action, ship working integrations early and improve them in production, and pull in teammates when the problem crosses into expertise you don't have.

What You'll Do

Build the Data Platform

Design, build, and operate containerized services that create, configure, and manage external data connections: databases, REST/SOAP APIs, file drops, message buses, and legacy systems of record

Own our open lakehouse: object storage, Apache Iceberg tables, a REST catalog with data versioning

Build batch and streaming ingestion with change data capture where it fits, and design it to run on disconnected, degraded, intermittent, and limited (DDIL) networks

Run the platform on Kubernetes across commercial cloud, government cloud, and air-gapped edge appliances using hardened, FIPS-validated images

Turn Raw Data into Trusted Entities

Build transformation pipelines that take source data through raw, cleaned, and curated layers into the semantic entity model Navigator and our AI agents run on

Implement entity resolution and data fusion so each ship, part, unit, or supply request resolves to one record across sources

Structure unstructured inputs (documents, slides, spreadsheets) into typed, relational records

Build lineage, provenance, data quality checks, and confidence labeling into every pipeline so downstream answers show their sources and confidence

Build Tools That Put Data in SME Hands

Build fullstack applications (TypeScript, Vue, Python services) that let SMEs connect sources, map fields, and compose pipelines with low-code workflows

Give users sandboxes to branch the data, test a pipeline change, compare results, and promote it to production

Build documented APIs that Navigator features and AI agents consume

Collaborate and Raise the Bar

Partner with Product, AI/ML, Frontend, and Infrastructure teams on RFCs, data models, and production code