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Engineering Team Lead

Trabajo disponible en: 08001, Barcelona, Cataluna, España
Empresa: Factorial HR
Tiempo completo puesto
Publicado en 2026-09-23
Especializaciones laborales:
  • Desarrollo de Software
    Desarrollador de full-stack, Desarrollador/a Back-end, Ingeniero de IA, Arquitecto de Software
Rango Salarial o Referencia de la Industria: 75000 - 110000 EUR Anual EUR 75000.00 110000.00 YEAR
Descripción del trabajo

Hello!

At Factorial, we're looking for an Engineering Team Lead who still writes code. You'll lead a small team of 4–7 engineers, stay deep in the codebase, and own a product area end to end.

To be clear about what this role is: it's a player-coach job. You review PRs, pair on hard problems, and ship features yourself, while also running 1:1s and helping the people around you grow. If you're looking for a role where you lead from a distance and spend the week in planning meetings, this isn't it.

We're at an interesting moment. AI isn't a feature we're adding to our product, it's changing how the product works. That means rethinking how we design and build software from the ground up, moving from deterministic systems to AI-powered workflows that help real businesses run better every day. It's genuinely hard, and it's exciting.

If you get energy from building alongside your team, working closely with the business, and using AI as a real tool rather than a buzzword — we'd love to meet you.

Ready to be part of the challenge?

About the team

Our Engineering team is the engine behind Factorial, crafting the tech that makes HR simple, fast, and reliable for thousands of companies. If you love solving hard problems and want to build something that truly matters, come build it with us.

We're a team of 200+ Product Engineers building a Business Management solution that actually works for the people using it. Our core stack is Ruby on Rails and React, but the interesting work right now is what we're building on top of that: agentic workflows, LLM-integrated features, systems that behave probabilistically rather than deterministically.

We believe that strong fundamentals, adaptability, and the ability to leverage modern tools, including AI, are what truly matter.

What are we looking for?
  • You're a strong engineer first, and you've started leading.
  • You've led a small team or acted as a Technical Team Lead, formally or informally. This can be your first role with the manager title.
  • You've mentored engineers, and you enjoyed it more than you expected.
  • You'd rather solve the problem with the team than hand it over and check in later.
  • You engage in honest, Socratic discussions focused on finding the best solution, not winning the argument.
Experience and technical depth
  • Solid full-stack experience shipping and maintaining production software.
  • You still code regularly, and you want to keep it that way.
  • Strong experience designing APIs (REST, GraphQL, etc.) and building integrations.
  • You've run into scalability, performance, and quality as real constraints in production, and worked through them.
  • You can read an unfamiliar codebase and be useful in it quickly.
Business mindset
  • Being a great Engineering Manager at Factorial means caring about more than the code. You understand the business context behind what you're building and can connect technical decisions to real outcomes for customers.
  • You're curious about how the business works and use that understanding to make better technical calls.
  • You push your team to understand the problem deeply before jumping to solutions.
  • You think in terms of customer value, not just technical deliverables.
  • You know when to ship something imperfect and when to slow down and get it right.
AI-native engineering and product mindset
  • We expect our engineering leads to build AI-powered products, not just use AI tools on the side.
  • You treat AI as a core part of modern software engineering, not something you bolt on at the end.
  • You've built or contributed to AI-powered features or workflows that made it to production.
  • You know what good AI development looks like in practice: defining behaviour and success criteria before you build, measuring output quality, grounding systems in real data, designing…
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