{"id":3026345,"url":"https://alion.io/job/gallatin-senior-data-engineer-data-platform","title":"Senior Data Engineer (Data Platform)","company":{"id":673248,"name":"Gallatin","domain":"gallatin.ai","url":"https://alion.io/company/gallatin","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"A","score":95,"open_postings":21,"ghost_share":0,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":257,"computed_at":"2026-10-22T06:10:10Z"}},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["El Segundo, United States","Washington, United States","San Francisco, United States","Austin, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":80000,"max":210000,"currency":"USD","period":"year","gross":null,"usd_annual":210000},"salary_estimate":null,"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Airflow","optional":false},{"name":"Amazon Aurora","optional":false},{"name":"Amazon S3","optional":false},{"name":"Apache Hudi","optional":false},{"name":"Apache Iceberg","optional":false},{"name":"Apache Kafka","optional":false},{"name":"Apache NiFi","optional":false},{"name":"Argo Workflows","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Dagster","optional":false},{"name":"Docker","optional":false},{"name":"Kubernetes","optional":false},{"name":"Presto","optional":false},{"name":"Python","optional":false},{"name":"React.js","optional":false},{"name":"Rest API","optional":false},{"name":"SOAP","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Trino","optional":false},{"name":"TypeScript","optional":false},{"name":"Vue.js","optional":false},{"name":"JavaScript","optional":true},{"name":"Knowledge Graph","optional":true},{"name":"Polaris","optional":true}],"status":"live","first_seen_at":"2026-10-21T14:38:03Z","employer_posted_date":"2026-10-21","last_verified_at":"2026-10-22T17:03:15Z","board_verified":true,"closed_at":null,"days_open":1,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":1},"description":"About Gallatin\nAt 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.\nWe 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.\nAbout the Role\nLogistics 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.\nWe'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.\nThe 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.\nSuccess 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.\nAbout You\nYou 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.\nYou'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.\nWhat You'll Do\nBuild the Data Platform\nDesign, 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\n\nOwn our open lakehouse: object storage, Apache Iceberg tables, a REST catalog with data versioning\n\nBuild batch and streaming ingestion with change data capture where it fits, and design it to run on disconnected, degraded, intermittent, and limited (DDIL) networks\n\nRun the platform on Kubernetes across commercial cloud, government cloud, and air-gapped edge appliances using hardened, FIPS-validated images\n\nTurn Raw Data into Trusted Entities\nBuild transformation pipelines that take source data through raw, cleaned, and curated layers into the semantic entity model Navigator and our AI agents run on\n\nImplement entity resolution and data fusion so each ship, part, unit, or supply request resolves to one record across sources\n\nStructure unstructured inputs (documents, slides, spreadsheets) into typed, relational records\n\nBuild lineage, provenance, data quality checks, and confidence labeling into every pipeline so downstream answers show their sources and confidence\n\nBuild Tools That Put Data in SME Hands\nBuild fullstack applications (TypeScript, Vue, Python services) that let SMEs connect sources, map fields, and compose pipelines with low-code workflows\n\nGive users sandboxes to branch the data, test a pipeline change, compare results, and promote it to production\n\nBuild documented APIs that Navigator features and AI agents consume\n\nCollaborate and Raise the Bar\nPartner with Product, AI/ML, Frontend, and Infrastructure teams on RFCs, data models, and production code\n\nWork with mission partners and field teams to map how data moves across their systems and translate operational constraints into platform requirements\n\nContribute to code reviews, documentation, and CI/CD practices, and mentor engineers as the data team grows\n\nWhat We're Looking For\nData Engineering Depth\n8+ years of professional data engineering experience, including hands-on design and operation of production data pipelines and ownership of a data platform or lakehouse\n\nHands-on production experience with an open table format (Apache Iceberg preferred; Delta or Hudi fine) and a distributed query engine (Trino, Presto, or Spark SQL), including partitioning, compaction, and schema evolution\n\nDemonstrated experience with streaming and batch data pipeline technologies (Kafka, NiFi, Debezium, Spark, or similar)\n\nStrong proficiency in Python and SQL, with experience integrating and validating data from multiple heterogeneous sources (relational and NoSQL databases, APIs, files, and streams)\n\nStrong data modeling skills for both analytical and operational workloads; experience designing entity models for complex real-world domains\n\nProduction orchestration experience (Airflow, Dagster, Argo Workflows, or similar)\n\nProduction experience writing services in Python and/or TypeScript, plus enough front-end skill (Vue, React, or similar) to ship a usable internal tool end to end\n\nComfortable designing and documenting RESTful APIs that other teams build on\n\nContainers and Platform\nStrong Kubernetes and Docker experience: you've packaged, deployed, and operated stateful data services on Kubernetes and can debug networking, storage, and namespace issues\n\nComfortable building on open-source tooling for self-hosted and air-gapped deployments\n\nExperience with AWS data services (e.g., S3, Glue, Aurora) or Azure equivalents and containerized environments (Kubernetes), delivered through DevSecOps/CI-CD pipelines\n\nCommunication and Growth Mindset\nDemonstrated ability to work independently against well-defined tasking with minimal oversight, and to communicate effectively with both technical and non-technical stakeholders, including data providers and consumers\n\nClear communicator across disciplines, whether writing an RFC, explaining a data model to an SME, or recording a weekly demo of recent work (a Gallatin tradition)\n\nAlways seeking to learn and stay current with industry trends and tools\n\nBonus Points\nActive SECRET or TOP SECRET clearance\n\nData catalog, lineage, and governance experience (Iceberg REST catalogs, Nessie, Polaris, OpenLineage, DataHub, or similar)\n\nData versioning and branching (Nessie, lakeFS) for sandboxed pipeline testing\n\nApache NiFi or other low-code/no-code pipeline tools, including custom processors\n\nEntity resolution, semantic data fusion, knowledge graphs, or ontology modeling\n\nShipping data workloads into air-gapped or classified environments\n\nPalantir Foundry or ontology experience\n\nData infrastructure for AI: metadata and retrieval that make agents more accurate and explainable\n\nOperations research, optimization, or constraint-solver background\n\nBackground in defense logistics, sustainment, or supply chain systems\n\nContributions to open-source data projects\n\nWhy Gallatin?\nJoin a mission-driven, high-impact, and fast-moving startup where your work directly improves defense logistics readiness for the warfighters of the United States and allied forces.\nWork alongside a team of passionate engineers, designers, and industry experts.\nCompetitive compensation incl. generous options grant, 100% employer-paid health insurance premiums, 401k, opportunities for rapid career growth, unlimited PTO, free lunches and snacks in the office.\nThis position requires the ability to obtain and maintain relevant security clearances. The successful candidate must be able to work in a classified environment when necessary.\nIf you're not excited about working hard, and solving the hard problems of building AI decision-support systems that enhance our warfighters, don't apply.\nThe following ranges are based on the cost of labor across US geographic areas. 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