Data Engineer
Build and own the data platform that powers our risk models, from raw events to clean, reliable data products.
Turning lost disputes into recovered revenue.
Guzco builds the infrastructure European e-commerce merchants use to detect, prevent and resolve fraud, chargebacks and payment disputes before they become financial losses.
Every payment method, card network and carrier has its own evidence requirements, formats and deadlines. A merchant selling via Klarna, PayPal, Mastercard, Visa and Amex is facing a different rulebook for each one. Most merchants write the losses off, not because the cases can't be won, but because responding correctly at that volume is impossible by hand.
We connect directly to payment providers, shop platforms, carriers and returns systems, turn fragmented order data into structured evidence, and file representments automatically inside each provider's deadline. Existing tools were built for US card flows and don't take the work over end to end.
Guzco is backed by investors with deep payment and e-commerce knowledge, works with merchants across Europe, and is based in the centre of Amsterdam.
Architect of our entire data platform.
As our Data Engineer, you build our data infrastructure from the ground up. This is not a role for maintaining legacy pipelines. It is a greenfield opportunity to define how we ingest, store and process large volumes of data.
You take raw, real-time and often messy data from payment providers, carriers and merchants, and turn it into the clean, high-performance data products that our models, our Machine Learning Engineer and our Product Engineers run on.
What you'll do.
Define and drive the data engineering roadmap in close partnership with the founders.
Design, build and own our ingestion and processing pipelines, capable of handling millions of real-time events.
Build the data warehouse and data lake that serve as the single source of truth for our risk models.
Partner with our Machine Learning Engineer on the data-serving and MLOps infrastructure needed to build, deploy and monitor models.
Go beyond notebooks. Own production systems, data quality and reliability.
Build clean, automated and observable systems that are a pleasure to work with.
Data-heavy. You decide how data flows through the platform, and you own its quality.
What we're looking for.
4+ years of experience in data engineering, platform engineering or MLOps, preferably in a high-volume environment.
Fluent in Python (used across our stack) and SQL, with deep experience in data architecture, ETL/ELT and data modeling.
Hands-on experience with cloud data platforms (especially GCP), stream processing (e.g. Kafka, Beam) and modern cloud infrastructure (e.g. Kubernetes, Cloudflare).
Obsessed with reliability and performance. You get energized by making a complex system fast, clean and unbreakable.
A builder who shows, not tells. You prove how good you are by shipping working solutions.
Thrives on autonomy and ownership. You want guidance, not guardrails.
A listener and a collaborator who wants to build a common vision with a small team.
Fintech, payments, disputes, chargebacks, or e-commerce experience.
Experience with logistics, carrier or 3PL data.
Hands-on experience with Airflow and BigQuery in production.
Familiarity with security and compliance frameworks such as SOC 2 or ISO 27001.
Excited about what we are building but don't tick every box? We'd still like to hear from you. The bar is high, but it is a bar on judgment and ownership, not on a checklist.
Small team. Hard problems.
We are a small team solving a problem that is unglamorous, expensive and genuinely hard. Here is what we value.
You set your own goals, make the calls in your domain, and see the direct financial impact of your work on our customers.
Pragmatic over perfect, but never careless. We are handling other people's money and other people's data.
You will be surrounded by people who set high standards for themselves and each other, and who treat obstacles as the work rather than as bad luck.
A market-rate salary and meaningful equity.
Direct impact on product, company and culture at a stage where all three are still being decided.
An office in the centre of Amsterdam, and a team that actually likes being there.
Yearly offsite with the team.
25 vacation days per year.
Learning and development budget.
How we hire.
Designed to assess mutual fit across skills, judgment and values.
A short conversation to align on your motivation, what you have built, and initial fit for the role.
A close look at your problem-solving approach through a technical challenge, case study, or role-specific scenario.
Meet team members across functions to explore collaboration dynamics, team fit, and day-to-day context.
A discussion about scope, equity, expectations and long-term alignment.
Interested?
If you want to build and own the data platform a new product runs on, we'd love to hear from you.
