Data Platform Engineer

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

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.

We'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.

If you like systems that reward good judgment over heroics, you'll 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

We're not looking for someone who's only ever worked inside a CI/CD pipeline someone else built.

We'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.

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.

That's the job. Not maintaining what exists - making it something the rest of engineering can pick up without you.

Why ShippyPro

• You’ll 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 else's
• You’ll move across data engineering, backend, and infrastructure - not stuck in one lane
• You’ll work alongside a Data & AI team that already has strong patterns in place, so you’re 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 You’ll 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 it's 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 You’ll Bring

The one thing we won't compromise on:

• 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. Having worked on a team that had CI isn't the same thing

Close behind:

• You can open unfamiliar code, explain what it does, and point at what's likely to bite - we'll 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 isn't a new concept (we use SageMaker among other things, but you don't need to have touched it)
• Comfort moving between data