Data engineer (Databricks)

Design, develop, and maintain scalable ETL/ELT pipelines using Databricks, build and optimize data workflows, implement data ingestion from multiple sources, and work with cloud platforms.

Requirements

• Design, develop, and maintain scalable ETL/ELT pipelines using Databricks.
• Build and optimize data workflows using Apache Spark (PySpark/Scala).
• Implement data ingestion from multiple sources (APIs, databases, streaming platforms).
• Develop and manage data lakes and lakehouse architectures.
• Work with cloud platforms such as Amazon Web Services, Microsoft Azure, or Google Cloud Platform.
• Optimize performance of queries and large-scale data processing jobs.
• Ensure data quality, governance, and security best practices.
• Collaborate with data scientists, analysts, and business stakeholders to deliver data solutions.
• Implement CI/CD pipelines and version control for data engineering workflows.