Role Description 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. 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? - Own production data pipelines end-to-end - Ensure reliability, performance, and scalability across the entire lifecycle. - Build and optimize data solutions - Develop pipelines and applications using PySpark and Foundry tools to process massive datasets efficiently. - Champion data quality - Design monitoring, validation frameworks, and health checks to guarantee trusted data. - Keep the platform running smoothly - Monitor workflows, troubleshoot issues, and respond to incidents with a focus on long-term improvements. - Act as the first line of support - Help users resolve data, access, and pipeline issues quickly and effectively. - Drive operational excellence - Conduct root cause analysis and implement automation to prevent recurring issues. - Collaborate across teams - Work closely with Foundry admins, engineers, and business stakeholders to define standards and improve the platform. - Shape the way we work with data - Contribute to best practices in data engineering, governance, and platform usage. Qualifications - 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 - Language: English – Upper intermediate (B2), German - Advantage Requirements - 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 Benefits - Financial benefits - Benefits with focus on learning and development - Benefits with focus on health and sport - Benefits with focus on family and work – life balance - Other benefits Final salary is negotiable. We are offering base salary depending on seniority level and previous experience of candidate. In addition to base salary we provide variable part and other financial benefits. Base salary will not be lower than 1650€ /brutto. Please be informed that our remote working possibility is only available within Slovakia due to European taxation regulation. Location: Remote from Slovakia

• Manage and maintain cloud infrastructure on GCP • Operate and troubleshoot Kubernetes environments • Support CI/CD pipelines using ArgoCD and GitHub Actions • Monitor platform health and respond to production incidents • Automate operational tasks using Terraform and scripting • Drive infrastructure optimization, reliability, and cost efficiency

• Implementación y mantenimiento de pipelines de CI/CD para automatizar los despliegues, bajo los lineamientos del equipo • Apoyo en el aprovisionamiento y la administración de la infraestructura cloud principal (Azure) mediante Terraform • Soporte y resolución de incidencias en entornos de Google Cloud Platform (GCP) cuando el proyecto lo requiera • Monitoreo y apoyo en el aseguramiento de la disponibilidad de los clústeres de Kubernetes (AKS) • Coordinación con los equipos de desarrollo sobre aspectos técnicos y de despliegue • Apoyo en la identificación y propuesta de mejoras en seguridad, rendimiento y optimización de costos en la nube.

• You are the technical owner for the infrastructure and core components that run n8n and related AI workflow tools—environments, CI/CD, containers, runtime clusters, storage, secrets, networking, and integrations—ensuring they are resilient, scalable, and cost‑effective. • You embed security, privacy, and compliance into how AI workflows are built and run: hardened baselines, secret management, network and IAM boundaries, and CI/CD guards that prevent unsafe changes from reaching production. • You define and drive SLOs, metrics, logging, and alerting for the AI workflow platform, turning incidents into systematic improvements that reduce MTTR and change failure rates over time. • You create and enforce reusable patterns (templates, reference pipelines, IaC modules, guardrails) so teams building on n8n and other tools follow consistent, auditable practices instead of bespoke one‑offs. • You partner with architecture, security, and platform teams to align AI workflow infra with Avalara's Software Maturity Model and engineering governance, moving the platform and guiding teams to higher levels of maturity. • You elevate how teams build and operate automations by mentoring engineers, codifying best practices, and using AI tools yourself to materially improve speed, quality, and reliability of the AI workflow platform.