h3CALLING FOR: Data Platform Engineer (Mid-level) /h3 pWe have built a data and ML platform that actually works: pipelines, infrastructure-as-code, deployment paths, governance standards baked in. /p pNow we need someone to turn that foundation into something any engineering team can build on directly, without waiting on you for every request. /p pYou 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. /p pIf you've owned a deployment path end-to-end – written it, broken it, fixed it, documented it – keep reading /p h3About Us /h3 pShippyPro was founded in 2016 on a simple idea: make shipping effortless so businesses can focus on growth. /p pToday we power shipping for thousands of merchants across 60+ countries, and our data platform is what keeps that running at scale — every label, every tracking update, every carrier integration leaves a trace, and our systems need to handle that without blinking. /p pWe've raised $15M (Series B) and we're scaling fast in a $9T industry still full of inefficiencies. Behind the product, there's a Data AI team building the infrastructure that makes reliability possible — clean pipelines, sane governance, and tooling that doesn't require an engineer to babysit it. You'll work within this team, reporting to our Data Team Leader. /p pIf you like systems that reward good judgment over heroics, you'll fit right in. /p h3The Product /h3 pShippyPro is a shipping and fulfillment platform that helps merchants automate the entire shipping workflow, from choosing the best carrier service to generating labels and tracking deliveries. It connects with e-commerce platforms and multiple couriers, giving teams one place to ship faster, reduce manual work, and keep full control over costs and delivery performance. /p h3The Challenge /h3 pWe're not looking for someone who's only ever worked inside a CI/CD pipeline someone else built. /p pWe're looking for someone who's built one, broken it, fixed it at 2am, and written the runbook so nobody else has to repeat that. /p pOur data platform works. The next phase is making it self-serve: a template repo that ships with CI, IaC and governance already wired, modules that make a new pipeline a one-command job, and documentation that actually answers the question instead of pointing at a person. /p pThat's the job. Not maintaining what exists — making it something the rest of engineering can pick up without you. /p h3Why ShippyPro /h3 ul liYou'll own real infrastructure from week one — no sandbox,



no toy projects /li liBy month six, the template repo and IaC modules have your name on them: your call on the roadmap, your PRs reviewed like everyone else's /li liYou'll move across data engineering, backend, and infrastructure — not stuck in one lane /li liYou'll work alongside a Data AI team that already has strong patterns in place, so you're building on solid ground, not from scratch /li liWe use AI tools daily (Copilot, Claude, Cursor) - and we care about whether you can defend what they produce, not just how fast you shipped it /li /ul h3What You’ll Do /h3 h3From week one (~40% of the role): /h3 ul liRead our existing CI/CD pipelines well enough to judge what a new task actually requires - most of the time it's a small adaptation of something that already exists, and knowing that is the skill /li liExtend our infrastructure-as-code following the patterns already in the repo /li liApply our data engineering and governance standards to new services: naming conventions, access control, retention /li /ul h3Ramping up from month two: /h3 ul liTurn those patterns into self-serve tooling: a template repo with CI, IaC and governance pre-wired, modules that make a new pipeline a one-command job, documentation that answers the question instead of pointing at a person /li liOwn the access-request flow for data resources so other teams stop queueing behind an engineer /li /ul h3By month six: /h3 ul liOwn the template repo and IaC modules outright — your name in the docs, your call on the roadmap /li /ul h3What You’ll Bring /h3 h3The one thing we won't compromise on: /h3 ul liYou've independently owned a deployment path end to end — you wrote the pipeline, broke production with it, fixed it, and wrote the runbook afterwards. Having worked on a team that had CI isn't the same thing /li /ul h3Close behind: /h3 ul liYou can open unfamiliar code, explain what it does, and point at what's likely to bite - we'll test this directly /li /ul h3Beyond that: /h3 ul li~2 years of professional experience, or more /li liPython and SQL you can work in daily without constantly looking things up /li liDocker,



and enough cloud exposure that AWS isn't a new concept (we use SageMaker among other things, but you don't need to have touched it) /li liComfort moving between data engineering, backend, and infrastructure work rather than staying in one lane /li /ul h3Worth saying plainly, so you can self-select: /h3 ul liThis isn't a frontend role, and there's no frontend component to it /li liIt's not an ML research role - you won't be training models /li liWe don't expect domain or tool knowledge on day one, so its absence isn't a reason to skip applying /li /ul pOn AI tooling: we use it and expect you to - Copilot, Claude, Cursor, whatever works for you. What we care about is whether you can defend the output. If you can't explain why generated code is correct, or notice when it's confidently wrong, the speed is worthless to us. Our interview process is built around that distinction. /p h3What Makes You a ShippyProer /h3 ul liYou read before you rewrite — you respect existing patterns before deciding they need to change /li liYou take ownership seriously — "not my code" isn't in your vocabulary once you've touched it /li liYou're honest about what broke and why, not just about what shipped /li liYou're comfortable being tested on your reasoning, not just your output /li /ul h3Why Join Us /h3 ul liCompetitive salary between €33,000 and €43,000, calculated through our salary simulator - built on objective metrics, because we believe in unbiased compensation /li liMeal vouchers (office or remote) /li liMental health support fitness benefits /li liYearly learning budget and AI tools /li liRemote flexibility with expenses-paid trips to HQ for team meetups /li liNo clock-in/out policy and one-time home office allowance /li liBirthday Time Off - one extra day off, just for you! /li liCareer Growth Program - clear growth paths, structured goals, and continuous feedback /li liAn international team that moves fast and cares about building things well /li /ul h3Hiring Process /h3 ol liIntro call with the PC team: deep dive on your background and what you've owned + a short reasoning exercise (non-technical) /li liPractical take-home exercise: a small existing codebase, capped at 90 minutes; use whatever tools you normally work with, AI included /li liTechnical conversation: 75 minutes on what you submitted and the reasoning behind it /li liTeam Lead conversation: how you like to work and what you want next /li /ol pWe give feedback either way after step three. /p pThanks for considering joining our team. We look forward to hearing from you! /p #J-18808-Ljbffr

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