[Hiring] Data Platform Engineer @ShippyPro

Role Description

We have built a data and ML platform that actually works: pipelines, infrastructure-as-code, deployment paths, governance standards baked in. Now we need someone to turn that foundation into something any engineering team can build on directly, without waiting on you for every request.

You won't get a blank page. You'll get working patterns — and the job is to read them, apply them, and turn them into tooling other people use without thinking twice.

If you've owned a deployment path end-to-end – written it, broken it, fixed it, documented it – keep reading.

Qualifications

• ~2 years of professional experience, or more
• Python and SQL you can work in daily without constantly looking things up
• Docker, and enough cloud exposure that AWS isn't a new concept
• Comfort moving between data engineering, backend, and infrastructure work
• Ability to open unfamiliar code, explain what it does, and point at what's likely to bite

Requirements

• You've independently owned a deployment path end to end — you wrote the pipeline, broke production with it, fixed it, and wrote the runbook afterwards
• This isn't a frontend role, and there's no frontend component to it
• It's not an ML research role — you won't be training models
• On AI tooling: we use it and expect you to — Copilot, Claude, Cursor, whatever works for you

Benefits

• Competitive salary between €33,000 and €43,000, calculated through our salary simulator
• Meal vouchers (office or remote)
• Mental health support & fitness benefits
• Yearly learning budget and AI tools
• Remote flexibility with expenses-paid trips to HQ for team meetups
• No clock-in/out policy and one-time home office allowance
• Birthday Time Off — one extra day off, just for you!
• Career Growth Program — clear growth paths, structured goals, and continuous feedback
• An international team that moves fast and cares about building things well

Hiring Process

• Intro call with the P&C team: deep dive on your background and what you've owned
• Practical take-home exercise: a small existing codebase, capped at 90 minutes
• Technical conversation: 75 minutes on what you submitted and the reasoning behind it
• Team Lead conversation: how you like to work and what you want next