About Tree-Nation
Tree-Nation is the world's largest reforestation platform. Since 2006, over 700,000 users and 20,000 companies have planted more than 40 million trees across 50+ reforestation projects worldwide.
We are a mature, self-funded platform with a real product, real users, and measurable environmental impact. We build seriously, without bureaucracy, and we are growing the team that keeps the platform solid as we scale.
The Role
We are hiring a Senior Platform Engineer to own the backend architecture of a specific operational domain — designing its data models, service layer, and integration points, then building and maintaining them in production.
You will work alongside a tech team of 3–5 engineers and report directly into the core of the company. Depending on your background, your domain could be environmental operations (project evaluation, monitoring, impact data flows) or revenue operations (CRM architecture, B2B pipeline logic, integration monitoring).
One important thing to be clear about: you are joining a system that already exists. There is a platform, a database, Business Units, running integrations. Your job is diagnosis first — understanding what is there before proposing what should change. If you also bring experience with AI tooling or agentic workflows, there is real opportunity to apply it here. But strong platform engineering comes first.
What You'll Do
Diagnose before you design
* Map the operational flows of your domain: what is manual, fragile, repeated, or ripe for automation
* Identify where a well-designed service can own a task end-to-end with traceable inputs and outputs
* Define system scope precisely — not 'fix the pipeline' but 'service that qualifies inbound leads based on company size, integration type, and engagement signal, routing to human review when confidence is below threshold'
Architect domain services
* Design modular backend services: discrete units with defined inputs, business rules, and outputs — isolated from the complexity of the whole system
* Ensure each service is independently deployable, observable, and replaceable
* Design the data model first — structure your database schema as the operational backbone before writing application code
* Structure data flows between internal systems, the platform database, CRMs, billing, and external APIs
Build and ship in production
* Implement backend services and APIs with clear orchestration logic and well-defined human control points
* Build full-stack when needed: from data model to API to the interface a supervisor uses to monitor and correct system behavior
* Prototype fast. Then industrialize. Use whatever stack solves the problem — we do not have a sacred stack (we use TypeScript and modern backend frameworks)
* Own observability and failure handling from the start — not as an afterthought
Own your domain
* Be the responsible engineer for your domain's architecture — the same way a Product Domain owner is accountable for their area
* Contribute to how the domain's KPIs are defined and tracked
* Align technical decisions with business logic: every architectural choice has an operational consequence
What We're Looking For
Platform engineering (required)
* Strong backend engineering background — you have designed, built, and shipped production systems end-to-end
* Deep database design thinking: you model data before you write code, and you understand how schema decisions propagate into downstream behavior
* Experience with modular service architecture — systems that are scoped, isolated, composable, and replaceable
* Solid system design instincts: APIs, state management, integration patterns, failure handling, and observability
* Tool-agnostic: you choose the right tool for the problem and are comfortable building custom when off-the-shelf does not fit
AI tooling (a strong plus)
* Curiosity about applied AI and how LLM-powered components can fit into production backend systems
* Practical exposure to LLM integration: context management, prompt reliability, output validation, latency constraints
* Experience or experimentation with multi-agent workflows, automated pipelines, or agentic task orchestration
* Ability to reason about control and failure modes in AI workflows — what happens when the model is wrong, slow, or unavailable
Problem-solving approach
* You diagnose before you prescribe — the right solution starts with the right question
* You can sit with ambiguity long enough to understand a problem fully before proposing a solution
* You choose AI tooling when it is the right fit for the specific problem, not because it is the interesting choice
Product and business thinking
* You can translate a business process into a system design — and back
* You understand that architecture defines operational behavior: a bad data model creates bad decisions downstream
* You are comfortable talking to non-technical stakeholders, asking operational questions, and making system decisions that reflect business reality
Who you are
* Engineer first — you solve problems with the right tool, whether that is a clean data model, a well-designed API, or an AI component
* Ownership mentality — you do not wait for scope to be defined; you define it
* Systems thinker — you see the whole before you optimize the part
* Based in Barcelona and comfortable working on-site
Your First 90 Days
* Understand the system. Map your domain. Interview the humans who currently run the workflows you will eventually systematize. Identify the three highest-leverage points for engineering intervention. Days 1–30
* Design the architecture for the first service. Define data models, inputs, business rules, outputs, failure modes, and human control points. Align with the tech team and ship a first working version into production. Days 30–60
* Iterate based on real usage. Define the KPIs for your domain with relevant stakeholders. Begin scoping the next system. Days 60–90
What We Offer
* Competitive base salary + performance-based bonus
* Permanent contract
* On-site role in Barcelona
* Direct technical ownership of a strategic domain in a global platform
* A small team that builds seriously, without bureaucracy
* Hands-on access to the latest AI tools and frontier technologies
* Work with real environmental impact at scale