Data Platform Engineer
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 **** 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, backe
/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 **** 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, backe