Full Stack Engineer - Eu (Barcelona Relocation)

Papaya is building the intelligence layer for vehicle fleets.Fleets spend billions every year, but most of that spend is poorly understood. Data is fragmented across leasing providers, fuel, maintenance, telematics, and charging. There’s no single source of truth. Costs don’t reconcile. Decisions are reactive. Even large organisations struggle to answer simple questions:What are we actually spending?Where are we overspending, and why?Are we being charged correctly?This has always been a hard problem. What’s changed is that it’s now solvable. We have real customers, real fleet data, and a clear industry need. At the same time, the AI tooling needed to make sense of this data is just becoming viable. The next generation of fleet software won’t just store information. It will understand what is happening, explain why it matters, and help teams act.What we’re buildingPapaya turns messy fleet data into something companies can trust and act on.At the core, we:Ingest real-world data (often CSVs, not clean APIs)Standardise it into a single, consistent modelProvide a clear, explainable view of total cost of ownershipBut the goal isn’t just visibility. It’s action. We’re building systems that operate directly on the data and take on real workflows:Reconciling discrepancies across suppliers when numbers don’t matchInvestigating billing issues and drafting dispute casesDetecting anomalies across thousands of vehiclesEnforcing policy and contract complianceA big part of this is building and orchestrating agents that can do this work reliably and that our customers can trust. You’ll work across the full stack on problems where data is incomplete, inconsistent, and sometimes wrong.Typical work includesDesigning and evolving the canonical data modelBuilding pipelines that handle messy, real-world inputsDefining how the system resolves conflicting dataBuilding and orchestrating agents that reason over the data and take actionDesigning how those agents behave, fail, and recoverMaking outputs explainable and auditable (not just “LLM says so”)Shipping features end-to-end, from idea to productionAbout the roleThis is as much a product role as it is engineering. You won’t just implement decisions, you’ll shape how the system behaves.BenefitsCompetitive salary of €40,000 to €80,000Meaningful equityApple hardwareBarcelona HQ in front of the beach (Norrsken House)Generous holiday + public holidaysTeam off?site in fun places! (We’ve been to Girona, Lisbon and Wales so far)QualificationsStrong full?stack ability (stack doesn’t matter)Comfortable with ambiguity and messy dataAble to take problems from idea to shipped solutionProduct mindset: focused on outcomes, not just codeStrong opinions, loosely heldLocationThis role is based in Barcelona. We’re open to candidates relocating from elsewhere in Europe and can support the move for the right person.

Strong full?stack ability (stack doesn’t matter) Comfortable with ambiguity and messy data Able to take problems from idea to shipped solution Product mindset: focused on outcomes, not just code Strong opinions, loosely held

Competitive salary of €40,000 to €80,000 Meaningful equity Apple hardware Barcelona HQ in front of the beach (Norrsken House) Generous holiday + public holidays Team off?site in fun places! (We’ve been to Girona, Lisbon and Wales so far)

Papaya is building the intelligence layer for vehicle fleets. Fleets spend billions every year, but most of that spend is poorly understood. Data is fragmented across leasing providers, fuel, maintenance, telematics, and charging. There’s no single source of truth. Costs don’t reconcile. Decisions are reactive. Even large organisations struggle to answer simple questions: What are we actually spending? Where are we overspending, and why? Are we being charged correctly? This has always been a hard problem. What’s changed is that it’s now solvable. We have real customers, real fleet data, and a clear industry need. At the same time, the AI tooling needed to make sense of this data is just becoming viable. The next generation of fleet software won’t just store information. It will understand what is happening, explain why it matters, and help teams act. What we’re building Papaya turns messy fleet data into something companies can trust and act on. At the core, we: Ingest real-world data (often CSVs, not clean APIs) Standardise it into a single, consistent model Provide a clear, explainable view of total cost of ownership But the goal isn’t just visibility. It’s action. We’re building systems that operate directly on the data and take on real workflows: Reconciling discrepancies across suppliers when numbers don’t match Investigating billing issues and drafting dispute cases Detecting anomalies across thousands of vehicles Enforcing policy and contract compliance A big part of this is building and orchestrating agents that can do this work reliably and that our customers can trust. You’ll work across the full stack on problems where data is incomplete, inconsistent, and sometimes wrong. Typical work includes Designing and evolving the canonical data model Building pipelines that handle messy, real-world inputs Defining how the system resolves conflicting data Building and orchestrating agents that reason over the data and take action Designing how those agents behave, fail, and recover Making outputs explainable and auditable (not just “LLM says so”) Shipping features end-to-end, from idea to production