Design and implement highly efficient data processing solutions for large-scale and diverse datasets using Databricks.
Design, build, and enhance data pipelines leveraging Python and modern cloud-native tools.
Work closely with Solution Architects to jointly define and uphold the best market practices in data engineering.
Ensure data consistency, security, and scalability within distributed cloud-based environments.
Solid commercial experience in Data Engineering, with hands-on expertise in Databricks.
Strong proficiency in Python (focused on automation and data transformation).
Experience working with at least one major cloud platform: AWS, Azure, or GCP.
Strong communication skills and excellent command of the English language.
Nice-to-Have:
SQL – solid understanding, including query optimization and data modeling.
DevOps approach – familiarity with CI/CD pipelines and Infrastructure as Code (Terraform, Bicep).
Experience with real-time data streaming technologies (e.g., Kafka, Spark Streaming).
Knowledge of cloud storage solutions and data warehouses (e.g., Data Lake, Snowflake, Synapse).
PySpark – hands-on experience in distributed data processing.
Industry certifications
