Senior Azure Data Engineer

Xebia; Polska; On-site; PLN 180,000 - 280,000; Full time

Job summary

Xebia is seeking an experienced Data Engineer to lead the design, development, and operation of scalable data pipelines for reporting and analytics. You will work with data professionals, architects, product managers, and engineers to translate needs into robust solutions, while upholding governance and platform standards.

Responsibilities include onboarding Core Products data workloads, ensuring reliability and observability, and driving DataOps practices across the Data Platform.

Qualifications

• 8+ years of professional experience in Data Engineering.
• Hands-on experience with production-grade data pipelines.
• Strong Azure and Databricks expertise.
• Advanced SQL and Python skills.
• Experience with data platforms and lakehouse concepts.
• Knowledge of DataOps, CI/CD, monitoring, and incident management.
• Strong stakeholder communication across multiple teams.

Responsibilities

• designing, building, testing, and deploying scalable data pipelines for reporting and analytics use cases
• creating new, production-ready pipelines supporting priority reporting use cases
• reviewing existing reporting pipelines and refactoring them to improve reliability, performance, maintainability, and observability
• maintaining new and existing pipelines to ensure data assets are reliably delivered on a daily basis
• working with Data professionals, Data Architects, Product Managers, and Engineering Managers to translate reporting needs into robust technical solutions
• contributing hands-on to the onboarding of Core Products data workloads onto the Data Platform
• ensuring pipelines are aligned with the architecture, governance, security, and engineering standards of the Data Platform
• ensuring compliance with platform standards for data modelling, code quality, testing, CI/CD, documentation, security, and monitoring
• owning SRE practices for data pipelines and products
• diagnosing data incidents and pipeline failures, identifying root causes, and implementing durable corrective actions
• documenting data flows, dependencies, transformation logic, and operating procedures
• ensuring clear documentation, monitoring, and operational ownership to support maintainability and reliable handover
• sharing knowledge and engineering practices with the Core Products data team and contributing to its long-term autonomy

Skills

Data engineering

Azure

Databricks

SQL

Python

Data pipelines

Data modelling

DataOps & CI/CD

SRE practices

Stakeholder comms

Tools

Microsoft Azure

Databricks

Job description

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 Millennium, ING, Play, Edenred, Arabian Drilling, FedEx, Leroy Merlin, Truecaller, Volotea, Schmitz Cargobull, 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, testing, and deploying scalable data pipelines for reporting and analytics use cases,
• creating new, production-ready pipelines supporting priority reporting use cases,
• reviewing existing reporting pipelines and refactoring them to improve reliability, performance, maintainability, and observability,
• maintaining new and existing pipelines to ensure data assets are reliably delivered on a daily basis,
• working with Data professionals, Data Architects, Product Managers, and Engineering Managers to translate reporting needs into robust technical solutions,
• contributing hands-on to the onboarding of Core Products data workloads onto the Data Platform,
• ensuring pipelines are aligned with the architecture, governance, security, and engineering standards of the Data Platform,
• ensuring compliance with platform standards for data modelling, code quality, testing, CI/CD, documentation, security, and monitoring,
• owning SRE practices for data pipelines and products,
• diagnosing data incidents and pipeline failures, identifying root causes, and implementing durable corrective actions,
• documenting data flows, dependencies, transformation logic, and operating procedures,
• ensuring clear documentation, monitoring, and operational ownership to support maintainability and reliable handover,
• sharing knowledge and engineering practices with the Core Products data team and contributing to its long-term autonomy.

Your profile:

• at least 8 years of professional experience in Data Engineering, including ownership of production-grade data pipelines,
• strong, demonstrable hands-on experience with Microsoft Azure and Databricks,
• advanced SQL and Python skills,
• strong experience designing, building, and operating data pipelines and analytical data products,
• a good command of modern data-platform and lakehouse principles, including medallion architecture and dimensional modelling for reporting,
• experience with software engineering and DataOps practices, including version control, automated testing, CI/CD, deployment, monitoring, and incident management,
• experience with SRE practices and continuous operational improvements,
• strong stakeholder communication skills and the ability to collaborate across Data, Architecture, Product, and Engineering teams,
• data modelling skills and the ability to develop a holistic view of different data scopes and business objectives before making build decisions,
• being able to work autonomously, make sound technical decisions, and document them clearly.

Nice to have:

• experience with data governance, lineage, and access-control practices in Databricks, including Unity Catalog,
• experience with payment or transaction data,
• experience working in distributed international teams.

Recruitment Process:

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