Principal Enterprise Data Engineer

FICO (NYSE: FICO ) is a leading global analytics software company, helping businesses in 100+ countries make better decisions. Join our world-class team today and fulfill your career potential! The Opportunity 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. What We’re Seeking 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. Our Offer to You An inclusive culture strongly reflecting our core values: Act Like an Owner, Delight Our Customers and Earn the Respect of Others. The opportunity to make an impact and develop professionally by leveraging your unique strengths and participating in valuable learning experiences. Highly competitive compensation, benefits and rewards programs that encourage you to bring your best every day and be recognized for doing so. An engaging, people-first work environment offering work/life balance, employee resource groups, and social events to promote interaction and camaraderie. Why Make a Move to FICO ? At FICO , you can develop your career with a leading organization in one of the fastest-growing fields in technology today – Big Data analytics. You’ll play a part in our commitment to help businesses use data to improve every choice they make, using adv