Data Engineer with Databricks

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. 

• • build, maintain, and optimize data pipelines using Python, Databricks, and PySpark for batch and real-time processing,
  • integrate data from multiple internal and external sources into the organization’s analytical platform,
  • maintain, enhance, and support the analytical database and the surrounding data ecosystem,
  • collaborate with business stakeholders and cross-functional teams to deliver data products that meet business needs and timelines,
  • identify technical debt and continuously improve platform reliability, maintainability, and performance,
  • build and manage Databricks Workflows for large-scale data orchestration,
  • develop, deploy, and maintain PyFunc models within production environments,
  • implement secure secrets and configuration management using Azure Key Vault,
  • automate data flows and business processes using Azure Logic Apps,
  • design, develop, and optimize ETL and ELT pipelines following data engineering best practices,
  • govern and manage data assets using Unity Catalog and Data Lakehouse principles,
  • implement messaging and event-driven workflows using Azure Service Bus,
  • collaborate closely with architects, analysts, data scientists, and engineering teams to deliver end-to-end data solutions,
  • contribute to CI/CD processes and deployment automation using Azure DevOps,
  • ensure high quality through testing, monitoring, troubleshooting, and continuous improvement activities,
  • depending on seniority, take ownership of technical areas, support architectural decisions, and mentor less experienced team members,

• minimum 3 years of commercial experience in software engineering, data engineering, or related fields,
• experience working in senior engineering, technical leadership, or highly autonomous delivery roles,
• strong hands-on knowledge of Python as the primary development language,
• strong experience with Databricks and PySpark in production environments,
• proven experience building and supporting modern data analytics platforms,
• practical experience working within Azure cloud environments or similar cloud ecosystems,
• experience designing modular, reusable, scalable, and maintainable system components,
• strong understanding of data engineering best practices and modern data platform architectures,
• familiarity with Medallion Architecture or equivalent data modeling and design patterns,
• ability to gather, clarify, and translate business requirements into technical solutions,
• strong problem-solving, communication, and organizational skills,
• proactive approach to identifying issues, opportunities, and improvement areas,
• experience working with Azure DevOps and CI/CD practices,
• excellent communication skills and ability to collaborate with technical and business stakeholders,
• experience working within Agile delivery environments and engineering best practices,
• quick learner with a strong interest in new technologies and continuous professional development,

• Practical experience using AI-powered assistants (e.g. Claude Code, GitHub Copilot, Cursor) to improve productivity, quality, or decision-making in software delivery.

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

• Databricks certification,
• experience with Infrastructure as Code technologies such as Terraform or ARM templates,
• experience with MLOps practices and automation of machine learning and data pipelines,
• experience working with Kubernetes or other container orchestration platforms,
• requirements engineering experience and business analysis awareness,
• familiarity with event-driven architectures and distributed data processing systems,

• 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.

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