Requirements
- 3+ years of experience in data engineering
- strong proficiency in Python and SQL for data processing and transformation
- hands-on experience with Spark/PySpark and distributed data processing
- hands-on experience developing data pipelines using Databricks notebooks
- experience building and maintaining pipelines on Databricks or similar cloud data platforms (Databricks strongly preferred)
- solid understanding of ETL/ELT design, data modeling, and workflow orchestration
- experience with Azure DevOps for code management, CI/CD, and release processes
- familiarity with orchestration tools such as Azure Data Factory (ADF)
- experience working with large-scale datasets and optimizing performance
- familiarity with modern data architecture concepts (e.g., layered/medallion approaches, data lake/lakehouse patterns)
- Understanding of data governance, data quality, and security fundamentals
- experience working in a structured SDLC environment, including version control and deployment practices
- ability to work both independently and within a team, with strong problem-solving skills
- exposure to AI-assisted development tools and workflows within the data engineering lifecycle
- exposure to streaming or near real-time data processing
- experience contributing to technical proposals, solution design, or platform improvements
Nice to Have
- familiarity with Databricks ecosystem features and recent platform capabilities
- experience with data platform governance tools (e.g., cataloging, lineage, access control)
- awareness of emerging data/AI platform capabilities (e.g., feature stores, data apps, conversational analytics, or pipeline automation tools)
- basic understanding of machine learning workflows and how data pipelines support them
Work Arrangement
Remote (Worldwide)
Additional Information
- Work from the European Union region and a work permit are required
