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About the Role

The Data Platform team connects fragmented data sources, cleans and models the data, and serves it to Navigator and AI agents. This role will design and ship containerized services that create and manage external data connections, run an open lakehouse, and power data transformations. The platform must operate across various environments, including commercial and government cloud, high-side enclaves, and air-gapped appliances, requiring open-source, Kubernetes-native components and data movement that tolerates intermittent networks.

Responsibilities

Design, build, and operate containerized services for managing external data connections (databases, APIs, files, message buses, legacy systems).
Own the open lakehouse, including object storage, Apache Iceberg tables, a REST catalog with data versioning, and federated query through Trino.
Build batch and streaming ingestion pipelines with change data capture, designed for disconnected, degraded, intermittent, and limited (DDIL) networks.
Run the platform on Kubernetes across diverse environments (commercial cloud, government cloud, air-gapped edge appliances) using hardened, FIPS-validated images.
Build transformation pipelines to convert source data into curated, semantically modeled entities for Navigator and AI agents.
Implement entity resolution and data fusion to create unified records across sources.
Structure unstructured inputs (documents, slides, spreadsheets) into typed, relational records.
Incorporate lineage, provenance, data quality checks, and confidence labeling into pipelines.
Build fullstack applications (TypeScript, Vue, Python services) for data source connection, field mapping, and low-code pipeline composition.
Provide users with sandboxes for branching data, testing pipeline changes, comparing results, and promoting to production.
Build documented APIs for consumption by Navigator features and AI agents.
Partner with Product, AI/ML, Frontend, and Infrastructure teams on RFCs, data models, and production code.
Collaborate with mission partners and field teams to map data flows and translate operational constraints into platform requirements.
Contribute to code reviews, documentation, and CI/CD practices.
Mentor engineers as the data team grows.

Requirements

8+ years of professional data engineering experience, including hands-on design and operation of production data pipelines and ownership of a data platform or lakehouse.
Hands-on production experience with an open table format (Apache Iceberg preferred; Delta or Hudi acceptable) and a distributed query engine (Trino, Presto, or Spark SQL), including partitioning, compaction, and schema evolution.
Demonstrated experience with streaming and batch data pipeline technologies (Kafka, NiFi, Debezium, Spark, or similar).
Strong proficiency in Python and SQL.
Experience integrating and validating data from multiple heterogeneous sources (relational and NoSQL databases, APIs, files, and streams).
Strong data modeling skills for both analytical and operational workloads; experience designing entity models for complex real-world domains.
Production orchestration experience (Airflow, Dagster, Argo Workflows, or similar).
Production experience writing services in Python and/or TypeScript.
Sufficient front-end skill (Vue, React, or similar) to ship a usable internal tool end to end.
Comfortable designing and documenting RESTful APIs.
Strong Kubernetes and Docker experience: packaging, deploying, and operating stateful data services on Kubernetes, debugging networking, storage, and namespace issues.
Comfortable building on open-source tooling for self-hosted and air-gapped deployments.
Experience with AWS data services (e.g., S3, Glue, Aurora) or Azure equivalents and containerized environments (Kubernetes), delivered through DevSecOps/CI-CD pipelines.
Demonstrated ability to work independently against well-defined tasking with minimal oversight.
Ability to communicate effectively with both technical and non-technical stakeholders, including data providers and consumers.
Clear communicator across disciplines.
Always seeking to learn and stay current with industry trends and tools.
Ability to obtain and maintain relevant security clearances.
Ability to work in a classified environment when necessary.

Skills

Python
SQL
React
Vue
TypeScript
Kubernetes
Docker
Apache Iceberg
Delta
Hudi
Trino
Presto
Spark SQL
Kafka
NiFi
Debezium
Spark
Airflow
Dagster
Argo Workflows
RESTful APIs
AWS data services
Azure data services
DevSecOps
CI/CD
Data catalog
Data lineage
Data governance
Nessie
lakeFS
Apache NiFi
Entity resolution
Semantic data fusion
Knowledge graphs
Ontology modeling
Palantir Foundry
Palantir Apollo
AI infrastructure
Operations research
Optimization
Constraint solvers
Defense logistics
Sustainment systems
Supply chain systems

Location

El Segundo, CA
Austin, TX
Washington, DC
San Francisco, CA

Work Type

In-office

Experience Level

Senior
8+ years of professional data engineering experience

Benefits

Generous options grant
100% employer-paid health insurance premiums
401k
Opportunities for rapid career growth
Unlimited PTO
Free lunches and snacks in the office

About the Company

Gallatin is rebuilding defense logistics for the warfighters of the United States and allied forces using an AI-first approach to improve defense readiness through software products that streamline and modernize logistics operations.
We believe in working hard and solving hard problems to build AI decision-support systems that enhance warfighters.