Azure Data 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:

• designing, building, and maintaining scalable data pipelines supporting analytics, reporting, and AI/ML use cases,
• developing and optimizing data ingestion and transformation workflows across multiple source systems,
• implementing data models following best practices (e.g., layered/medallion-style architectures) to support different consumption patterns,
• developing pipelines using Databricks notebooks and integrating them into production workflows using orchestration and CI/CD tools,
• ensuring data quality, reliability, and performance through testing, monitoring, and validation practices,
• contributing to data governance, security, and access controls in line with platform standards,
• collaborating with analytics, product, and engineering teams to deliver data solutions aligned with business needs,
• supporting and enhancing CI/CD and SDLC practices for data pipelines, including adoption of automation and AI-assisted development tools,
• participating in evaluating and piloting new platform capabilities and tools within the data ecosystem,
• contributing to defining and improving standards for data democratization, discoverability, and reusable data assets,
• troubleshooting and resolve data pipeline and performance issues in production environments.

Your profile:

• 3+ years of experience in data engineering,
• strong proficiency in Python and SQL for data processing and transformation,
• hands-on experience with Spark/PySpark and distributed data processing,
• hands-on experience developing data pipelines using Databricks notebooks,
• experience building and maintaining pipelines on Databricks or similar cloud data platforms (Databricks strongly preferred),
• solid understanding of ETL/ELT design, data modeling, and workflow orchestration,
• experience with Azure DevOps for code management, CI/CD, and release processes,
• familiarity with orchestration tools such as Azure Data Factory (ADF),
• experience working with large-scale datasets and optimizing performance,
• familiarity with modern data architecture concepts (e.g., layered/medallion approaches, data lake/lakehouse patterns) • Understanding of data governance, data quality, and security fundamentals,
• experience working in a structured SDLC environment, including version control and deployment practices,
• ability to work both independently and within a team, with strong problem-solving skills,
• exposure to AI-assisted development tools and workflows within the data engineering lifecycle,
• exposure to streaming or near real-time data processing,
• experience contributing to technical proposals, solution design, or platform improvements.

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

Nice to have:

• familiarity with Databricks ecosystem features and recent platform capabilities,
• experience with data platform governance tools (e.g., cataloging, lineage, access control),
• awareness of emerging data/AI platform capabilities (e.g., feature stores, data apps, conversational analytics, or pipeline automation tools),
• basic understanding of machine learning workflows and how data pipelines support them

Recruitment Process:

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