designing and implementing a cloud-based data platform using Microsoft Fabric,
building and maintaining platform components, CI/CD pipelines, and infrastructure as code,
supporting data engineers, analysts, and data scientists by providing stable and scalable platform services,
ensuring security, monitoring, cost optimization, and operational best practices,
collaborating closely with data, cloud, and architecture teams,
as an alternative stack, contributing to solutions based on Azure Databricks where applicable.
Your profile:
strong experience as a Platform Engineer / DevOps Engineer,
hands-on experience with Microsoft Azure (compute, networking, storage, security),
practical experience in building and operating data platforms (especially analytics-focused),
very good understanding of Microsoft Fabric or strong experience with Azure Databricks,
experience with infrastructure as code (e.g. Terraform),
knowledge of CI/CD pipelines and automation Good understanding of data concepts (data lakes, data warehouses, ETL/ELT, analytics workloads),
ability to work independently and take ownership of platform-related topics,
knowledge of monitoring, logging, and alerting in cloud environments,
understanding of security and governance in data platforms,
practical experience using AI-powered assistants (e.g. Claude Code, GitHub Copilot, Cursor) to improve productivity, quality, or decision-making in software delivery,
ready to join within 2 weeks.
Work from the European Union region and a work permit are required.
Nice to have:
experience with Microsoft Purview or data governance tools,
familiarity with FinOps / cost optimization in Azure,
background in supporting data engineering or analytics teams,
experience working in international or distributed teams,
Experience working in enterprise-scale environments,
experience applying GenAI in a more structured way within the SDLC, including defined workflows, prompt patterns, or tool integrations embedded into daily work,
interest in and familiarity with emerging AI-driven practices (e.g. agent-based workflows, automation patterns, AI-augmented development), with a willingness to explore and experiment beyond standard approaches.
