Data Engineering TraineeData Engineering Trainee

Basic knowledge of Python programming and a strong willingness to further develop Python skills. Understanding of fundamental database concepts and SQL. Basic understanding of data engineering, ETL/ELT processes, or data analytics concepts. Basic understanding of analytical databases and cloud-based data platforms is a plus. Ability to leverage AI-assisted development tools to enhance productivity and accelerate learning. Exposure to DBT, Dagster, Apache Airflow, Snowflake, or similar technologies would be an advantage. Familiarity with CI/CD concepts and tools such as GitLab is a plus. AWS knowledge (especially S3, SNS, and related services) would be a plus. Good knowledge of English, both spoken and written. Strong analytical thinking, problem-solving skills, and eagerness to learn and grow in the Data Engineering field. The Data Engineering Trainee will learn through hands-on experience and guidance from experienced Data Engineers. He/She will gain a solid understanding of modern data engineering practices, data pipelines, cloud-based data platforms, and analytics infrastructure. The trainee will contribute to building and maintaining data solutions that support business intelligence and data-driven decision-making while continuously developing technical and professional skills. Key Responsibilities Assist in developing, maintaining, and improving data pipelines and ETL/ELT processes. Support the implementation and maintenance of data transformation workflows using tools such as DBT or similar technologies. Participate in orchestrating and monitoring data workflows using platforms such as Dagster, Apache Airflow, or similar tools. Work with cloud-based data warehouses, such as Snowflake, and help ensure data quality, reliability, and performance. Collaborate with Data Engineers, Analysts, and other stakeholders to understand data requirements and implement data solutions. Assist in automating manual data-related tasks and operational processes. Support CI/CD practices for data engineering projects using GitLab or similar tools. Contribute to documentation, knowledge sharing, and continuous improvement initiatives. The paid traineeship is up to 6 months, and you will gain: Hands-on Data Engineering experience working on real-world data platforms and analytics solutions. Opportunity to work with modern data engineering technologies, including ETL/ELT tools, workflow orchestration platforms, cloud data warehouses, and CI/CD pipelines. Practical experience in building, maintaining, and optimizing data pipelines that support business intelligence and data-driven decision-making. Mentorship from experienced Data Engineers and exposure to modern cloud and AI-assisted engineering practices.

Basic knowledge of Python programming and a strong willingness to further develop Python skills. Understanding of fundamental database concepts and SQL. Basic understanding of data engineering, ETL/ELT processes, or data analytics concepts. Basic understanding of analytical databases and cloud-based data platforms is a plus. Ability to leverage AI-assisted development tools to enhance productivity and accelerate learning. Exposure to DBT, Dagster, Apache Airflow, Snowflake, or similar technologies would be an advantage. Familiarity with CI/CD concepts and tools such as GitLab is a plus. AWS knowledge (especially S3, SNS, and related services) would be a plus. Good knowledge of English, both spoken and written. Strong analytical thinking, problem-solving skills, and eagerness to learn and grow in the Data Engineering field. The Data Engineering Trainee will learn through hands-on experience and guidance from experienced Data Engineers. He/She will gain a solid understanding of modern data engineering practices, data pipelines, cloud-based data platforms, and analytics infrastructure. The trainee will contribute to building and maintaining data solutions that support business intelligence and data-driven decision-making while continuously developing technical and professional skills. Key Responsibilities Assist in developing, maintaining, and improving data pipelines and ETL/ELT processes. Support the implementation and maintenance of data transformation workflows using tools such as DBT or similar technologies. Participate in orchestrating and monitoring data workflows using platforms such as Dagster, Apache Airflow, or similar tools. Work with cloud-based data warehouses, such as Snowflake, and help ensure data quality, reliability, and performance. Collaborate with Data Engineers, Analysts, and other stakeholders to understand data requirements and implement data solutions. Assist in automating manual data-related tasks and operational processes. Support CI/CD practices for data engineering projects using GitLab or similar tools. Contribute to documentation, knowledge sharing, and continuous improvement initiatives. The paid traineeship is up to 6 months, and you will gain: Hands-on Data Engineering experience working on real-world data platforms and analytics solutions. Opportunity to work with modern data engineering technologies, including ETL/ELT tools, workflow orchestration platforms, cloud data warehouses, and CI/CD pipelines. Practical experience in building, maintaining, and optimizing data pipelines that support business intelligence and data-driven decision-making. Mentorship from experienced Data Engineers and exposure to modern cloud and AI-assisted engineering practices.