**Roles and Responsibilities** * Design and implement scalable, reliable, and efficient data pipelines for ingesting, processing, and storing large amounts of data from a variety of sources using cloud-based technologies, Python, and PySpark. * Build and maintain data lakes, data warehouses, and other data storage and processing systems on the cloud. * Write and maintain ETL/ELT jobs and data integration scripts to ensure smooth and accurate data flow. * Implement data security and compliance measures to protect data privacy and ensure regulatory compliance. * Collaborate with data scientists and analysts to understand their data needs and provide them with access to the required data. * Stay up-to-date on the latest developments in cloud-based data engineering, particularly in the context of Azure, AWS and GCP, and proactively bring new ideas and technologies to the team. * Monitor and optimize the performance of data pipelines and systems, identifying and resolving any issues or bottlenecks that may arise. **Qualifications** * Bachelor’s or Master’s degree in Computer Science, Data Science, or a related field. * Minimum of 5 years of experience as a Data Engineer, with a strong focus on cloud-based data infrastructure. * Proficient programming skills in Python, Java, or a similar language, with an emphasis on Python. * Extensive experience with cloud-based data storage and processing technologies, particularly Azure, AWS and GCP. * Familiarity with ETL/ELT tools and frameworks such as Apache Beam, Apache Spark, or Apache Flink. * Knowledge of data modeling principles and experience working with SQL databases. * Strong problem-solving skills and the ability to troubleshoot and resolve issues efficiently. * Excellent communication and collaboration skills to work effectively with cross-functional teams.