Data Platform Engineer (Mid-level)

CALLING FOR: Data Platform Engineer (Mid-level)

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 wont get a blank page. Youll get working patterns and the job is to read them apply them and turn them into tooling other people use without thinking twice.

If youve owned a deployment path end-to-end written it broken it fixed it documented it keep reading

About Us

ShippyPro was founded in 2016 on a simple idea: make shipping effortless so businesses can focus on growth.

Today 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.

Weve raised $15M (Series B) and were scaling fast in a $9T industry still full of inefficiencies. Behind the product theres a Data & AI team building the infrastructure that makes reliability possible clean pipelines sane governance and tooling that doesnt require an engineer to babysit it. Youll work within this team reporting to our Data Team Leader.

If you like systems that reward good judgment over heroics youll fit right in.

The Product

ShippyPro 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.

The Challenge

Were not looking for someone whos only ever worked inside a CI/CD pipeline someone else built.

Were looking for someone whos built one broken it fixed it at 2am and written the runbook so nobody else has to repeat that.

Our 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.

Thats the job. Not maintaining what exists making it something the rest of engineering can pick up without you.

Why ShippyPro

• Youll own real infrastructure from week one no sandbox no toy projects
• By month six the template repo and IaC modules have your name on them: your call on the roadmap your PRs reviewed like everyone elses
• Youll move across data engineering backend and infrastructure not stuck in one lane
• Youll work alongside a Data & AI team that already has strong patterns in place so youre building on solid ground not from scratch
• We 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

What Youll Do

From week one (40% of the role):

• Read our existing CI/CD pipelines well enough to judge what a new task actually requires most of the time its a small adaptation of something that already exists and knowing that is the skill
• Extend our infrastructure-as-code following the patterns already in the repo
• Apply our data engineering and governance standards to new services: naming conventions access control retention

Ramping up from month two:

• Turn 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
• Own the access-request flow for data resources so other teams stop queueing behind an engineer

By month six:

• Own the template repo and IaC modules outright your name in the docs your call on the roadmap

What Youll Bring

The one thing we wont compromise on:

• Youve 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 isnt the same thing

Close behind:

• You can open unfamiliar code explain what it does and point at whats likely to bite well test this directly

Beyond that:

• 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 isnt a new concept (we use SageMaker among other things but you dont need to have touched it)
• Comfort moving between data engineering backend and infrastructure work rather than staying in one lane

Worth saying plainly so you can self-select:

• This isnt a frontend role and theres no frontend component to it
• Its not an ML research role you wont be training models
• We dont expect domain or tool knowledge on day one so its absence isnt a reason to skip applying

On 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 cant explain why generated code is correct or notice when its confidently wrong the speed is worthless to us. Our interview process is built around that distinction.

What Makes You a ShippyProer

• You read before you rewrite you respect existing patterns before deciding they need to change
• You take ownership seriously not my code isnt in your vocabulary once youve touched it
• Youre honest about what broke and why not just about what shipped
• Youre comfortable being tested on your reasoning not just your output

Why Join Us

• Competitive salary between 33000 and 43000 calculated through our salary simulator built on objective metrics because we believe in unbiased compensation
• 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
• Want to know more Click here Process
  1. Intro call with the P&C team: deep dive on your background and what youve owned
  2. Practical take-home exercise: a small existing codebase capped at 90 minutes; use whatever tools you normally work with AI included
  3. Technical conversation: 75 minutes on what you submitted and the reasoning behind it
  4. Team Lead conversation: how you like to work and what you want next

  We give feedback either way after step three.

  A Note Before You Apply

  If you want to move on with the application youll have to answer a few questions. We love AI (its part of almost everything we do!) so feel free to use it. But make sure your answers still sound like you. Were not looking for polished corporate responses were looking for builders.

  Thanks for considering joining our team. We look forward to hearing from you!