Mid/Senior Data Engineer (AWS)

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, developing, and maintaining scalable batch and real-time data pipelines,

building ELT workflows and data transformations using Python, SQL, and dbt,

developing and maintaining cloud-based data infrastructure on AWS, including S3, Glue, Lambda, and Redshift,

implementing data orchestration and scheduling using Apache Airflow or Amazon MWAA,

working with streaming and big data technologies such as Kafka, Spark, and Apache NiFi,

ensuring the reliability, performance, scalability, and quality of production data pipelines,

collaborating with data architects, product teams, analysts, IT teams, and business stakeholders to deliver scalable data solutions,

contributing to data security, governance, monitoring, automation, and continuous improvement,

participating in a scheduled on-call rotation to support the reliability of the data platform.

Your profile:

3+ years of professional experience in data engineering or a related technical role,

hands-on experience with AWS and cloud-based data platforms,

strong SQL and data modelling skills,

experience with production-scale data warehouses such as Amazon Redshift, Snowflake, or BigQuery,

proven experience with Python, dbt, and Apache Airflow,

familiarity with Kafka, Spark, and Apache NiFi,

experience with Docker and Kubernetes,

strong analytical, problem-solving, communication, and collaboration skills,

comfortable working with large and complex datasets in production environments,

excellent English skills and being able to communicate effectively with international teams,

a proactive mindset and being focused on continuous improvement.

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

Recruitment Process:

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