Lead Data Platform Engineer

• Design and develop tools that standardize, automate, and simplify Data Platform operations.
• Build internal CLI tools, Data Product templates, and reusable AI automation components.
• Create Backstage visualizations and operational insights; analyze platform usage, audit, and adoption data.
• Design Agentic AI workflows, developer-assistance capabilities, and lifecycle management for AI skills.
• Maintain the Apache Airflow platform on Cloud Composer and build reusable DAG libraries and operators.
• Develop infrastructure-as-code assets and Terraform modules.
• Support GitOps adoption with Backstage, GitHub, ArgoCD, and Crossplane.
• Collaborate with platform, data engineering, analytics, and machine learning teams.

Key requirements:

• Strong commercial Python development experience.
• Hands-on experience with GCP, BigQuery, cloud data platforms, Apache Airflow, and preferably Cloud Composer.
• Experience with platform engineering, developer tooling, or internal self-service solutions.
• Knowledge of infrastructure as code, Terraform, GitOps, CI/CD, source control, data engineering, and Data Product lifecycle management.
• Experience with PySpark or Apache Spark, FastAPI, Pydantic, and Agile development.
• Strong problem-solving, independent work, communication, and cross-functional collaboration skills.
• Professional proficiency in Polish and English.
• Work from the European Union region and a valid work permit are required.

Nice to have:

• Vertex AI or other GenAI platforms, GitHub Copilot, and AI-assisted development solutions.
• Agentic AI workflows, Backstage plugin development, Dataproc, dbt, Kedro, ArgoCD, Crossplane, observability, telemetry, or platform analytics.
• Experience in large-scale data environments supporting analytics and machine learning workloads.