Lead 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. In the CEE region, they have 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, and InPost, working with technologies including AWS, Azure, GCP, Databricks, and Snowflake.

• Strong commercial experience with Python development.
• Hands-on experience with Google Cloud Platform (GCP).
• Practical experience with BigQuery and cloud-based data platforms.
• Experience with Apache Airflow, preferably Cloud Composer.
• Experience building platform engineering, developer tooling, or internal self-service solutions.
• Knowledge of Infrastructure as Code practices and Terraform.
• Experience with GitOps principles and modern software delivery practices.
• Good understanding of data engineering concepts and Data Product lifecycle management.
• Experience with PySpark and/or Apache Spark ecosystems.
• Familiarity with FastAPI, Pydantic, and modern Python tooling.
• Knowledge of CI/CD processes and source control best practices.
• Experience working in Agile development environments.
• Strong problem-solving skills and ability to work independently.
• Effective communication skills and ability to collaborate with cross-functional teams.
• Professional proficiency in Polish and English.

Nice to have:

• Experience with Vertex AI or other GenAI platforms.
• Hands-on experience with GitHub Copilot, Copilot extensions, plugins, or AI-assisted development solutions.
• Experience developing Agentic AI workflows or AI automation capabilities.
• Knowledge of Backstage plugin development.
• Experience with Dataproc.
• Familiarity with dbt and Kedro.
• Experience with ArgoCD and Crossplane.
• Experience building observability, telemetry, or platform analytics solutions.
• Experience working in large-scale data environments supporting analytics and machine learning workloads.

• Designing and developing tools that standardize, automate, and simplify Data Platform operations.
• Building and maintaining internal CLI tools for common platform and Data Product management tasks.
• Developing and evolving Data Product templates based on modern data engineering practices.
• Designing and implementing visualizations and operational insights within Backstage.
• Collecting and analyzing platform usage metrics, audit data, and adoption statistics.
• Designing and implementing Agentic AI workflows and developer-assistance capabilities.
• Developing reusable AI skills and automation components that standardize work with data products and data assets.
• Creating mechanisms for distribution, lifecycle management, and monitoring of AI skills.
• Maintaining and enhancing a shared Apache Airflow platform based on Cloud Composer.
• Building reusable libraries, operators, and common components for Airflow DAG development.
• Developing and maintaining infrastructure-as-code assets and Terraform modules.
• Contributing to GitOps adoption initiatives using tools such as Backstage, GitHub, ArgoCD, and Crossplane.
• Collaborating with platform, data engineering, analytics, and machine learning teams to improve platform usability and engineering efficiency.

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