Period: 01/06/2026 – 31/12/2026
Work Mode: Hybrid (approx. 20% onsite – 1 day per week in Warsaw, Poznań or Wrocław)
Experience Level: ~8 years
 

Role Overview:

We are looking for an experienced Data Engineer to join a modern data platform team within the Finance & Risk domain. The role focuses on designing, building, and maintaining scalable, cloud-based data solutions that support analytics, reporting, and machine learning initiatives.

You will work closely with engineering, analytics, and business stakeholders to deliver robust data pipelines and ensure high-quality, automated, and efficient data processing across the platform.
 

Key Responsibilities:

• Design, develop, and maintain scalable data pipelines using Python, PySpark, and SQL.
• Build and operate data solutions on Databricks Lakehouse architecture.
• Develop and manage data workflows using Apache Airflow.
• Implement CI/CD pipelines using Azure DevOps and Azure Pipelines.
• Work with Git using feature-branch workflows and pull request-based development.
• Automate, schedule, and monitor data processing jobs (e.g., Databricks Lakeflow Jobs).
• Package, deploy, and manage data solutions using Databricks Asset Bundles.
• Ensure data quality through automated testing frameworks (pytest, pre-commit hooks).
• Work within containerized development environments (Devcontainers, VS Code extensions).
• Collaborate with cross-functional teams to ensure reliable and efficient data delivery.
• Support continuous improvement of data platform performance, reliability, and scalability.

Required Skills & Experience:

• Strong experience in Python, including PySpark, pytest, and modern Python tooling (e.g., uv package manager).
• Hands-on experience with Databricks Lakehouse platform, including Lakeflow Jobs and Asset Bundles.
• Strong proficiency in SQL.
• Experience with Apache Airflow for workflow orchestration.
• Solid experience with Azure Cloud services.
• Practical experience with Azure DevOps and Azure Pipelines.
• Strong knowledge of Git workflows, including feature branching and pull request-based development.
• Experience in modern data engineering and DevOps practices.

Nice to Have:

• Experience in financial services or risk data environments.
• Familiarity with Agile methodologies (Scrum, Kanban).
• Experience working on large-scale enterprise data platforms.
• Exposure to cloud-native architecture patterns.
• Experience in DevOps-oriented engineering teams.
• Knowledge of monitoring, observability, and production support for data pipelines.

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