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Developer Experience - Engineering Manager

EburyMadridPosted 25 Sept 2026

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About this role

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.

Engineering Manager, Developer Experience (Platform & AI) Location: Madrid Work Model: Hybrid (4 days in-office / 1 day WFH) About the Team The Developer Experience (DevExp) team builds and operates the foundational platform powering all engineering at Ebury—from CI/CD, deployment, and test infrastructure to self-service tooling and enterprise AI systems across the development lifecycle. We treat our development process as a product, our engineers as customers, and success as measurable outcomes: lead time, change failure rate, and platform adoption.

Our North Star is simple: driving modern SDLC evolution, scaling AI adoption safely, and maximizing developer autonomy across the business. About the Role 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: The Delivery Platform (CI/CD & Infrastructure) The AI-Assisted Developer Platform In This Role, You Will Own the Delivery 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.

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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. Build the AI-Assisted Developer Platform 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). Lead and Scale the Team 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. You Might Thrive in This Role If... Platform Background: You have built or operated an Internal Developer Platform (Backstage or similar) at scale, backed by clear delivery and adoption metrics.

Leadership Experience: You have managed engineering teams or served as a Tech Lead with formal hiring, coaching, and performance management responsibilities. Core Tech Stack Proficiency: Deep expertise in AWS/GCP, Terraform, Kubernetes, ECS/EKS, GitHub Actions, Python, and Linux. Hands-on Engineering Roots: Background in backend software development, enabling you to design and build custom platform tools alongside senior Staff Engineers.

Daily AI User: You actively use coding agents daily and have brought AI tooling past the proof-of-concept stage into production (agent sandboxes, MCP servers, evaluation pipelines, or CI-integrated agents). Engineering Discipline: You are rigorous about foundational principles—trunk-based development, small batch sizes, version control hygiene, automated testing, and deep observability. Product Mindset: You measure platform success through data (DORA/SPACE frameworks) combined with qualitative developer research, pivoting strategy based on hard evidence.

Nice to Have FinTech & Regulated Industry Experience: Familiarity with SOC 2, GDPR, or the EU’s Digital Operational Resilience Act (DORA compliance framework). Open Source Contributions: Active contributions to platform or AI ecosystem projects (Backstage plugins, MCP servers, or agent tooling frameworks). Advanced AI System Knowledge: Hands-on experience with LLM evaluation pipelines, agent scoring frameworks, Spec-Driven Development, or tokenomics optimization.

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Source listing: greenhouse_ebury

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