Senior Everything Engineer (Data & Migrations)
@ Redhorse CorporationSenior Everything Engineer (Data & Migrations)
This job is still taking applications, but it's been up a while.
About the job
Redhorse delivers data insights and technology solutions supporting U.S. national interests. The Senior Full-Stack Software Engineer leads legacy system migration, data integrity, and modernization efforts, including AI-driven development and cloud infrastructure expertise.
Requirements
- 7+ years data systems experience
- Experience with AI tools (Claude, Codex)
- Relational database design and migration
- Web stack: TypeScript, Node.js, React
- U.S
Qualifications
- U.S
- citizen
- Experience with legacy database logic
- Cloud infrastructure knowledge (AWS)
- Strong problem-solving skills
Full job description
About the Role
Redhorse transforms the way government uses data and technology. We are seeking "Everything Engineers" with a Data Focus to lead the modernization of a mission-critical legacy system managing billions of dollars in international security cooperation.
The project
We’re working to modernize a mission-critical legacy system that manages foreign military sales cases for the U.S. government. It’s the system of record behind billions of dollars in international security cooperation. Under the hood: four Oracle schemas (three of them mirrored copies kept in sync by triggers over database links), roughly half a million lines of PL/SQL, hundreds of views and triggers, a large Cognos reporting estate, and decades of accumulated production data with every edge case you can imagine and several you can’t. It works. But we can do better for an important national security mission.
Our job is pretty straight forward. Out with the old, in with the new. We’re going to replace the legacy system with a modern web platform that does all the things; from a functional perspective, a technical perspective, and a compliance perspective. And we’re going to make it happen through quick iterations, built on an Agentic AI Software Development Lifecycle.
How we build (read this before applying)
AI-led development isn’t a novelty act, its just how software is built. Full stop. Every engineer on this team works through agentic toolkits, Codex/Claude Code/etc. Every day. On production code. Our engineering harness (versioned rules, skills, and specs that guide both humans and agents) is a first-class product with its own backlog. We measure ourselves on shipped, verified capability, not lines typed.
What that means in practice:
If you think of AI-led development as vibe-coding, or some fad you tolerate between “real” coding sessions, this isn’t your team. If you’ve hand rolled enough code to appreciate how Agentic AI outsources all the tedious bits and lets you build anything, let’s talk.
What “Everything Engineer” means
Everybody builds across the whole stack (UI, API, database, infrastructure, pipeline) because our unit of work is a complete vertical slice of business capability, not a layer. You’ll have genuine breadth: you can build the admin UI for your data quality reports, write an API endpoint, and reason about security controls. And you’ll have real depth in your focus area, where you set the technical bar for the team.
Your focus: Data
You’ll own the ground the whole modernization stands on.
Who thrives here
The stack
TypeScript end to end. Node with a REST API. PostgreSQL with Prisma. React front end. Temporal workflows. AWS GovCloud, defined in AWS CDK (TypeScript). GitHub and GitHub Actions CI. Vitest and Playwright. Codex and/or Claude Code (or equivalent) with a versioned team harness. Slack and Jira. Everything gets wrapped in containers and pushed to EKS or ECS. On the legacy side you’ll deal with: Oracle 19c, PL/SQL, converted-Forté Java, Cognos, Control-M.
Requirements
Desired Experience
Heterogeneous migration experience, ideally Oracle to Postgres, with validation and reconciliation rigor; data modeling for analytics; data-quality tooling.
Nice to have: prior work in a regulated or government environment; financial data and audit exposure; synthetic data generation; Cognos experience.
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