Data Platform Engineer (Mid-level)

Role Overview

Accountabilities

• Improve CI/CD pipelines by analyzing existing deployment pipelines and adapting established workflows for reliable, repeatable software delivery.
• Develop infrastructure-as-code by extending and maintaining infrastructure modules in line with existing repository patterns.
• Apply data governance standards including naming conventions, access controls, and retention policies.
• Build self-service platform tooling with a reusable template repository of preconfigured CI/CD, infrastructure-as-code, and governance standards.
• Create reusable modules that simplify pipeline creation and reduce repetitive engineering work.
• Improve technical documentation with practical documentation, runbooks, and troubleshooting guides.
• Own data access workflows by designing and managing access-request processes for data resources.
• Take ownership of platform tooling including technical direction, prioritization, maintenance, and roadmap decisions.
• Collaborate across engineering disciplines to improve platform reliability and make shared tools useful across teams.
• Use AI tools responsibly, validating generated code and explaining the reasoning behind technical decisions.

Requirements

• Demonstrated experience independently owning a deployment path end to end, including building a pipeline, troubleshooting failures, resolving production issues, and documenting the solution in a runbook.
• Approximately two years or more of professional experience in software, data, platform, DevOps, or infrastructure engineering.
• A degree in Computer Science, Engineering, or a related field.
• Practical working knowledge of Python and SQL usable regularly in professional engineering tasks.
• Experience working with deployment pipelines and infrastructure-as-code practices.
• Ability to navigate an unfamiliar codebase, explain how it works, identify potential failure points, and make sound technical judgments.
• Experience with Docker and familiarity with cloud computing concepts, particularly AWS; specific services such as SageMaker are not required.
• Willingness and ability to work across data engineering, backend systems, and infrastructure.
• Comfortable using AI coding assistants such as Copilot, Claude, or Cursor and able to review generated code critically.
• Proactive ownership and accountability, including honesty about failures, root causes, and lessons learned.
• Ability to understand existing architecture and engineering conventions before deciding what should change.
• Comfortable explaining technical decisions and being evaluated on reasoning as well as delivery.

Benefits

• Annual compensation ranging from €33,000 to €43,000, determined through an objective salary assessment process.
• Work remotely from anywhere worldwide, with company-funded trips to headquarters for team meetups.
• Meal vouchers available to remote and office-based employees.
• Mental health and fitness support benefits.
• Annual learning budget to support professional growth and technical development.
• Access to AI-powered tools for everyday engineering work and experimentation.
• One-time home office allowance to set up a productive workspace.
• No clock-in or clock-out policy, supporting autonomy and a focus on results.
• An additional day off to celebrate your birthday.
• Career Growth Program with clear development pathways, structured goals, and ongoing feedback.

Application Process

• This position is listed on behalf of a partner company, which manages all applications and next steps.
• Jobgether uses an AI-powered matching process to review applications against the role's core requirements before sharing a shortlist with the hiring company.