Principal Enterprise Data Engineer

Experteer Overview In this role you will architect and own the harness environment that guides AI-generated code to production-grade quality. You will collaborate with cross-functional teams to set standards, build verification and trust mechanisms, and evolve the end-to-end lifecycle of turning model capability into reliable software. You’ll shape the controls that keep AI outputs safe and maintainable, while advancing observability and release criteria. This is a hands-on, systems-focused opportunity at a leading analytics software company with global impact. Pay / Benefits * Design, build, deploy, and support harness core components (guides, feedback loops, guardrails) for production-grade engineering * Own and evolve feedforward guides and drive team-wide standardisation for correct agent behavior * Create feedback sensors (linters, static analysis, architecture-fitness tests) and verification loops * Define quality gating and release criteria for agent-produced work and escalation rules * Establish LLM testing infrastructure and evaluation approaches with contract testing (e.g. Pact) * Manage steering loop to prevent repeated mistakes and maintain system records of docs and context * Decide placement of controls across pre-commit, post-integration, and drift-monitoring stages * Improve observability and track metrics (PR velocity, defect escape, etc.) to guide investments * Partner with product/platform teams to translate specs into enforceable controls * Mentor engineers on harness practices and responsible use of AI tools Tasks * Bachelor’s/Master’s in Computer Science or related disciplines, or equivalent commercial experience * Seasoned software engineer with large, complex codebase experience and strong design/architecture focus * Hands-on experience with AI coding agents (e.g. Claude Code, Codex) and understanding of their limitations * Proven ability to build tooling across a modern stack (linters, static analysis, CI/CD, containerized environments, instrumentation) * Familiarity with agent instruction conventions (e.g. AGENTS.md) * Experience with spec-driven development, context engineering, agent orchestration, fitness functions, and developer-platform work * Systems mindset with ability to encode good practices into repeatable rules * Judgement on when to use deterministic controls vs. LLM-based approaches and trade-offs * Experience owning quality-gating processes and release criteria * Knowledge of security considerations for autonomous agents (prompt injection, least-privilege guardrails) * Experience with consumer/contract testing (e.g., Pact) to validate distributed service integrations * Excellent communication skills to articulate designs and drive standards Key requirements * Highly competitive compensation * benefits and rewards programs * work from home / remote (UK) * employee resource groups * social events * work/life balance

_ rules * Establish LLM testing infrastructure and evaluation approaches with contract testing (e.g. Pact) * Manage steering loop to prevent repeated mistakes and maintain system records of docs and context * Decide placement of controls across pre-commit, post-integration, and drift-monitoring stages * Improve observability and track metrics (PR velocity, defect escape, etc.) to guide investments * Partner with product/platform teams to translate specs into enforceable controls * Mentor engineers on harness practices and responsible use of AI tools Tasks * Bachelor’s/Master’s in Computer Science or related disciplines, or equivalent commercial experience * Seasoned software engineer with large, complex codebase experience and strong design/architecture focus * Hands-on experience with AI coding agents (e.g. Claude Code, Codex) and understanding of their limitations * Proven ability to build tooling across a modern stack (linters, static analysis, CI/CD, containerized environments, aaaaaa end-to-end * Familiarity with agent instruction conventions (e.g. AGENTS.md) * Experience with spec-driven development, context engineering, agent orchestration, fitness functions, and developer-platform work * Systems mindset with ability to encode good practices into repeatable rules * Judgement on when to use deterministic controls vs. LLM-based approaches and trade-offs * Experience owning quality-gating processes and release criteria * Knowledge of security considerations for autonomous agents (prompt injection, least-privilege guardrails) * Experience with consumer/contract testing (e.g., Pact) to validate distributed service integrations * Excellent communication skills to articulate designs and drive standards Key requirements * Highly competitive compensation * benefits and rewards programs * work from home / remote (UK) * employee resource groups * social events * work/life balance