Data Platform Engineer – Mid-level

🕒 October 4

🌏 Anywhere in the World

💵 €33k - €43k / year

⏰ Full Time

🟢 Junior

🟡 Mid-level

🚰 Data Engineer

🚫👨‍🎓 No degree required

👻 Ghost score 0%

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ShippyPro

WebsiteLinkedInAll Job Openings

51 - 200 employees

📦 Logistics

🛍️ eCommerce

☁️ SaaS

💰 $5M Series A on 2021-07

Logistics • eCommerce • SaaS

ShippyPro is a company that supports eCommerce brands by enhancing delivery speed and efficiency, as well as automating tracking and returns processes. Founded in 2016, ShippyPro is on a mission to revolutionize logistics by developing and launching innovative products that meet the delivery expectations of customers. The company continues to grow, expanding its team and product offerings to better serve its clientele. ShippyPro operates as a Software as a Service (SaaS) platform, helping businesses streamline their logistics operations with modern, scalable solutions.

📋 Description

• Read and evaluate existing CI/CD pipelines to determine task requirements • Adapt existing CI/CD patterns for new tasks • Extend infrastructure-as-code according to repository patterns • Apply data engineering and governance standards to new services, including naming conventions, access control, and retention • Build self-serve tooling from existing patterns • Create a template repository with CI, IaC, and governance pre-wired • Develop modules that make creating a new pipeline a one-command job • Write documentation that answers common questions without relying on an engineer • Own the access-request flow for data resources • Own the template repository and IaC modules and guide their roadmap

🎯 Requirements

• Independently owned a deployment path end to end, including writing the pipeline, fixing production issues, and documenting the runbook • Ability to open unfamiliar code, explain what it does, and identify likely problems • Approximately 2 years of professional experience • Python and SQL proficiency for daily use • Docker experience • Enough cloud exposure that AWS is not a new concept • Comfortable moving between data engineering, backend, and infrastructure work • Willingness to use AI tools such as Copilot, Claude, or Cursor and defend their output • This is not a frontend role and has no frontend component • This is not an ML research role and does not involve training models

🏖️ Benefits

• 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 • One-time home office allowance • Birthday Time Off — one extra day off • Career Growth Program — clear growth paths, structured goals, and continuous feedback

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