Data Platform DevOps Engineer (Palantir Foundry) (m/f/d)

Our brand Deutsche Telekom IT Solutions Slovakia entered the life of Košice region in 2006 under the name of T-Systems Slovakia and ever since has been inextricably linked with the region when became one of the founding members of Košice IT Valley. We have managed to grow from scratch to the second largest employer in the eastern part of the country with more than 3900 employees. Our goal is to proactively find new ways to improve and continuously transform into the type of company providing innovative information and communication technology services.

We’re looking for a Data Platform Engineer to help us design, build, and operate robust data solutions on Palantir Foundry. This is a high-impact, end-to-end role where engineering meets operations—you won’t just ship pipelines, you’ll own them in production and ensure they deliver reliable, high-quality data to users every day.

If you enjoy solving complex data problems, optimizing large-scale pipelines, and being the go-to expert who keeps things running smoothly—you’ll thrive here.

Purpose

DevOps Engineer is responsible for entire lifecycle of Continuous Integration/Continuous Deployment pipelines and Infrastructure as Code approaches. Takes account and defines automated configuration management, release management, build, test and deployment activities.

WHAT WILL YOU DO?

Ensure reliability, performance, and scalability across the entire lifecycle.

Develop pipelines and applications using PySpark and Foundry tools to process massive datasets efficiently.

Design monitoring, validation frameworks, and health checks to guarantee trusted data.

Monitor workflows, troubleshoot issues, and respond to incidents with a focus on long-term improvements.

Help users resolve data, access, and pipeline issues quickly and effectively.

Conduct root cause analysis and implement automation to prevent recurring issues.

Work closely with Foundry admins, engineers, and business stakeholders to define standards and improve the platform.

Contribute to best practices in data engineering, governance, and platform usage.

Language

- English – Upper intermediate (B2)
- German - Advantage

Don’t Meet Every Requirement?

We know that great candidates don’t always tick every box. If you’re excited about this role, passionate about working with data, and eager to learn new technologies like Palantir Foundry, we encourage you to apply anyway. We value curiosity, growth mindset, and the willingness to take ownership just as much as experience—so if this sounds like you, we’d love to hear from you.

Responsibilities

Own production data pipelines end-to-end; Build and optimize data solutions; Champion data quality; Keep the platform running smoothly; Act as the first line of support; Drive operational excellence; Collaborate across teams; Shape the way we work with data

Requirements

Have experience with data engineering / big data ecosystems; Have hands-on expertise with PySpark and distributed data processing; Have experience working with production data pipelines and troubleshooting; Have understanding of data quality, monitoring, and governance; Have ability to balance development and operational responsibilities; Have solid software development skills with a focus on writing clean, readable, and maintainable code in Python; Have experience with testing practices, including unit/integration testing and validating data pipelines; Are proficiency in version control systems (e.g., Git) and collaborative development workflows; Are familiar with designing and working with REST APIs; Have commitment to code quality, documentation, and best practices; Have strong problem-solving mindset and ownership attitude; Have experience with Palantir Foundry (or similar platforms) is a big plus; Willingness to learn new technologies; TypeScript, SQL; Good skills in integrating systems and applications; Good understanding of Cloud systems, DevOps concepts, and tooling; Experience with data platform reliability / DataOps practices; Familiarity with automation and workflow orchestration; Exposure to user-facing data applications or analytics tools; Ability to work both individually and in team; Being self-motivated and organized