Developer Experience - Engineering Manager

Ebury helps ambitious businesses unlock global growth, and we take the same approach with our people. We encourage innovation and movement, collaboration and problem-solving, and foster an environment where everyone can feel they belong, are valued, supported and empowered to succeed.

If you're a collaborator who wants to help transform how businesses operate globally, get in touch - we'd love to discuss how Ebury can accelerate your career so you can shape the future.

• Location: Madrid

• Work Model: Hybrid (4 days in-office / 1 day WFH)

You will lead this team as a player-coach. You will hire, mentor, and grow a team of platform engineers while staying technically grounded enough to drive direction on platform architecture, progressive delivery, and AI agent execution frameworks.

You will own two core mandates end-to-end-from design and architectural strategy through to day-to-day operations:

1. The Delivery Platform (CI/CD & Infrastructure)

2. The AI-Assisted Developer Platform

• Run CI/CD as a Product: Define and deliver against strict SLOs for the SDLC platform. Continuously innovate and propose infrastructure improvements based on developer feedback.

• Build Progressive Delivery into the Paved Path: Implement automated canary releases, real-time metric analysis, instant rollbacks, feature flag management, and safe database migrations across our AWS and GCP environments (Kubernetes, ECS/EKS).

• Evolve Observability & Self-Service Infrastructure: Provide developer self-service tooling in partnership with product engineering, including automated service scaffolding, a centralized software catalog with ownership scorecards, ephemeral PR preview environments, and standardized Terraform modules.

• Elevate Quality Gates: Maintain shared test infrastructure and automated quality guardrails to keep change failure rates low-even as overall code volume grows through AI-assisted engineering.

• Operate Ebury's Agent Harness: Build and run managed, sandboxed execution environments for coding agents (Claude CLI, Gemini CLI, and emerging successors) featuring isolated Git worktrees, short-lived scoped credentials, session audit logs, and multi-model routing to prevent vendor lock-in.

• Integrate AI Where It Pays Off: Deploy evaluated AI tooling into the SDLC for pull-request reviews, test generation, automated dependency/code migrations, internal docs, and incident summarization.

• Build Context Layers for Agents: Expose internal context via Model Context Protocol (MCP) servers over our software catalog, engineering standards, runbooks, ADRs, and internal docs.

• Implement Financial-Grade Guardrails: Engineer strict security guardrails tailored to a regulated financial institution, including sandboxed execution with egress controls, prompt-injection defenses, human-in-the-loop approval gates, and eval suites to prevent AI behavioral regression.

• Instrument the Entire SDLC: Track traditional DORA metrics alongside AI-specific telemetry (adoption rate, suggestion acceptance/reversal rates, agent session success, and review overhead).

• Hire & Coach: Recruit, grow, and retain high-performing platform engineers while remaining hands-on enough to review designs and unblock technical obstacles.

• Lead with Evidence: Drive the product roadmap using developer research, adoption metrics, and satisfaction surveys rather than reactive ticket queues.

• Set Ebury's AI Engineering Stance: Clear guidelines, permitted tools, and best practices that encourage fast experimentation within secure boundaries.

• Drive Enablement: Partner with Security, Compliance, and Core Engineering to ensure the safest path is always the fastest path through active documentation, training, and internal advocacy.