> Markdown version of [/jobs/ext/2415081-principal-enterprise-data-engineer](https://www.wearedevelopers.com/jobs/ext/2415081-principal-enterprise-data-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Principal Enterprise Data Engineer - **Company:** FICO - **Location:** UK (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Static Program Analysis, Code Review, Encodings, Distributed Systems, Software Architecture, Software Engineering, Enterprise Data Management, Large Language Models, Multi-Agent Systems, Information Technology, Build Process, Codebase, Software Coding - **Published:** August 10, 2026 - **Apply:** https://uk.indeed.com/viewjob?jk=487bc0685c60e2ad ## About the Role * Bachelor's/Master's in Computer Science or related disciplines, or relevant commercial experience in software architecture, design, development, and testing. * Seasoned software engineer with experience in large, complex codebases and a strong foundation in architecture and design; you care deeply about testing and maintainability. * Hands-on experience with AI coding agents (e.g. Claude Code, Codex, or similar) and a well-developed feel for where they succeed and fail. * Proven ability to build engineering tooling across a modern stack - linters and static analysis, CI/CD pipelines, containerised build/test environments, and instrumentation/observability - plus familiarity with agent instruction conventions such as AGENTS.md. * Experience with spec-driven development, context engineering, agent orchestration, fitness functions, and developer-platform work. * A systems mindset - you'd rather fix the environment than fix one output - and the ability to encode "what good looks like" into mechanical, repeatable rules. * Judgement about when to reach for deterministic, computational controls (type checkers, linters, structural/architecture-fitness tests) versus inferential, LLM-based ones (AI code review, LLM-as-judge) - and an understanding of the cost, speed, and reliability trade-offs between them. * Experience owning quality-gating processes and defining release criteria to ensure engineering standards are consistently met. * Working knowledge of the security surface unique to autonomous agents - prompt injection, tool/permission scoping, sandboxed execution, and audit trails for agent actions - and how to design least-privilege guardrails around them. * Experience with consumer/contract testing approaches (e.g. Pact) to validate service integrations across distributed systems. * Excellent communication skills; able to articulate design with architects and drive standards across teams. ## Description Come join our engineering team in a hands-on technical role at the heart of a new discipline: Harness Engineering. As AI coding agents take on more of the software lifecycle, the hard part is no longer writing code - agents generate it faster than humans can review it, so the bottleneck shifts to verification and trust. Harness Engineering exists to break that bottleneck: engineering the environment that steers agents toward correct, maintainable, well-architected output so that quality is enforced by the system, not re-audited by a person on every change. We call that environment the harness (Agent = Model + Harness). As a Senior Harness Engineer you'll independently own whole harness subsystems, set the standards other engineers build to, and be involved in the end-to-end lifecycle of turning raw model capability into production-grade engineering. What You'll Contribute * Design, build, deploy, and support core components of the harness - the guides, feedback loops, guardrails, and shared context that turn raw model capability into production-grade engineering. This is a hands-on role focused on systems and leverage, not hand-writing application code. * Own and evolve feedforward guides - agent instruction files, reusable skills, architectural rules, reference docs, and codemods - and drive team-wide standardisation so agents get it right the first time. * Build feedback sensors - custom linters, static analysis, structural and architecture-fitness tests, verification loops, and LLM-as-judge reviewers - that catch issues automatically before they reach human reviewers. * Own quality gating and release criteria for agent-produced work, defining authority boundaries for what agents may merge unaided and the escalation rules for what must route to a human. * Establish LLM testing infrastructure and evaluation approaches that ensure AI-generated output meets quality and safety thresholds; apply consumer/contract testing (e.g. Pact) where service integration reliability matters. * Run the steering loop - when an agent repeats a mistake, engineer a control so it can't happen again - and treat repository knowledge (docs, specs, context) as the system of record, fighting drift with continuous garbage collection. * Decide where each control runs in the path to production - fast checks pre-commit, more expensive checks post-integration, and continuous sensors that scan for drift outside the change lifecycle - keeping quality as far left as is economical. * Improve observability into agent work and track the measures that matter - cost per merged PR, time-to-merge for agent-assisted PRs, review velocity relative to PR size, defect escape rate, and agent-PR survival rate - using them to decide where to invest next. * Partner with product and platform teams to turn specifications and acceptance criteria into enforceable controls. * Serve as a source of technical expertise and mentor engineers across teams in harness practices and the effective, responsible use of AI tools. ## Related Videos - [Getting to Know Your Legacy (System) with AI-Driven Software Archeology](https://www.wearedevelopers.com/videos/1437-getting-to-know-your-legacy-system-with-ai-driven-software-archeology) - [A Brief History of Data Storage](https://www.wearedevelopers.com/videos/974-a-brief-history-of-data-storage) - [Are Code Reviews Worth It? Insights from 16 Years of Review Data](https://www.wearedevelopers.com/videos/1135-are-code-reviews-worth-it-insights-from-16-years-of-review-data) - [Agentic employees in world's most downloaded FinTech app](https://www.wearedevelopers.com/videos/100123-agentic-employees-in-world-s-most-downloaded-fintech-app) - [Why your codebase lies to AI?](https://www.wearedevelopers.com/videos/100281-why-your-codebase-lies-to-ai) - [Building a Multi-Agent Orchestration Engine That Actually Follows the Rules](https://www.wearedevelopers.com/videos/100159-building-a-multi-agent-orchestration-engine-that-actually-follows-the-rules) ## Related Articles - [What is Agentic Programming and Why Should Developers Care?](https://www.wearedevelopers.com/magazine/625-what-is-agentic-programming-and-why-should-developers-care) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [Never delegate the understanding](https://www.wearedevelopers.com/magazine/749-never-delegate-the-understanding) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [From Prototype to Production: Build AI Agents with This Free 4-Course Learning Path](https://www.wearedevelopers.com/magazine/655-from-prototype-to-production-build-ai-agents-with-this-free-4-course-learning-path)