Data Engineer

Design, implement, and maintain scalable and efficient data pipelines in one of the cloud environments (Azure, AWS, GCP) using tools such as Databricks, Glue, Dataflow, or Azure Data Factory

• Develop and optimize ETL/ELT processes using cloud-native services (e.g., Azure Data Factory, AWS Glue, GCP Dataflow) and Apache Spark/Databricks
• Build Big Data solutions aligned with business and analytical requirements across cloud platforms
• Collaborate with Data Science, BI, and development teams to deliver high-quality, well-structured, and performant data
• Monitor and improve the performance, reliability, and scalability of data processing systems
• Implement robust data governance, security standards, and best practices across cloud environments
• Research and evaluate new tools and technologies within the cloud and data engineering ecosystem

Minimum 3 years of experience as a Data Engineer or in a similar role

• Hands-on experience with one or more major cloud platforms (Azure, AWS, GCP); deep knowledge of cloud data services such as:
• Azure Data Factory, Azure Data Lake, Synapse Analytics (Azure)
• AWS Glue, S3, Redshift, Athena (AWS)
• GCP Dataflow, BigQuery, Cloud Storage (GCP)
• Extensive experience with Databricks and Apache Spark
• Proficiency in SQL and experience with relational and columnar databases
• Strong programming skills in Python and PySpark
• Experience designing and optimizing data pipelines in distributed, cloud-based architectures
• Familiarity with Delta Lake or other modern data lake architectures
• Solid understanding of data modeling and schema design