Azure Data Platform Engineer

Xebia is a global AI-first, digital transformation, and engineering partner. With over 25 years of experience and a team of 5,000 professionals across 16 countries, we help organizations design and build scalable products, platforms, and data-driven solutions. 

We specialize in Artificial Intelligence, Data and Cloud, Intelligent Automation, and Digital Products, combining deep technical expertise with a strong focus on engineering excellence and a people-first culture.  

In the CEE region, we’re a team of nearly 1,000 experts delivering modern applications, data platforms, and AI solutions for clients such as McLaren, Aviva, Deloitte, Spotify, Disney, ING, UPS, Tesco, Truecaller, AllSaints, Volotea, Schmitz Cargobull, Allegro, InPost, and many, many more. We work with leading technologies including AWS, Azure, GCP, Databricks, and Snowflake, and combine strong engineering culture with a consulting mindset and a continuous focus on growth and knowledge sharing. 

You will be:

• Participating in discovery workshops and technical assessments of the existing data platform environment.
• Defining platform requirements and contribute to migration strategy and roadmap planning.
• Collaborating with Data Engineers, Architects, and business stakeholders to design target-state architecture.
• Supporting migration of data workloads, pipelines, and platform services to Azure, Databricks, and/or Microsoft Fabric.
• Building and managing cloud infrastructure using Infrastructure as Code (IaC) practices with Terraform.
• Designing, deploying, and maintaining Kubernetes-based platform environments. 
• Developing and maintaining CI/CD pipelines for automated provisioning, deployment, and operations.
• Implementing security, governance, identity management, and access control standards.
• Establishing monitoring, observability, alerting, and operational processes.
• Optimizing reliability, scalability, performance, and cost efficiency of the platform.
• Producing technical documentation, standards, and operational procedures.
• Providing technical leadership and guidance to engineering teams throughout the migration journey.

Your profile:

• Strong hands-on experience with Microsoft Azure.
• Experience with Databricks and/or Microsoft Fabric.
• Good understanding of modern data platform architectures and data engineering ecosystems.
• Strong experience with Terraform and Infrastructure as Code (IaC) practices.
• Experience building and managing cloud infrastructure in enterprise environments.
• Hands-on experience with Kubernetes administration and platform operations.
• Strong Python development skills with experience building automation tools and operational utilities.
• Experience implementing and maintaining CI/CD pipelines and deployment automation.
• Solid understanding of platform engineering and DevOps best practices.
• Knowledge of cloud security principles, governance frameworks, and compliance requirements.
• Experience implementing identity and access management, security controls, auditing, and access governance.
• Understanding of data platform security, operational governance, and reliability best practices.
• Experience collaborating with Data Engineers, Architects, and cross-functional stakeholders.
• Strong communication, documentation, and stakeholder management skills.

Work from the European Union region and a work permit are required.

Nice to have:

• Hands-on experience delivering Microsoft Fabric implementations.
• Experience with large-scale data platform modernization or migration programs.
• Databricks administration, monitoring, performance tuning, and optimization.
• Experience with observability, monitoring, and alerting platforms.
• Familiarity with data governance tools and frameworks.
• Experience with dbt, Apache Airflow, or other modern data platform technologies.
• Exposure to FinOps practices and cloud cost optimization initiatives.
• Experience supporting platform adoption and engineering enablement across multiple teams.

Recruitment Process:

CV review – HR call – Interview – Client Interview – Decision