Data Engineer

As a data engineering professional, you will play a key role in designing and developing scalable data pipelines using Python and PySpark. Your responsibilities will include: - Ingesting, processing, and storing large volumes of structured and unstructured data to ensure data accuracy, integrity, and accessibility for analytical projects. - Collaborating with cross-functional teams to understand data needs and deliver data-driven solutions to meet business objectives. - Optimizing data models and queries for performance improvement, leveraging database technology to enhance data quality and performance. - Maintaining version control using Git and adhering to best practices in coding and documentation to ensure streamlined processes. - Monitoring and troubleshooting data workflows to meet reliability and performance standards. Qualifications for this role include: - Minimum of 5 years of experience in data engineering with a strong focus on Python and PySpark. - Proficiency in developing data pipelines and ETL processes, solid understanding of SQL databases, and data modeling concepts. - Experience with cloud technologies such as AWS, Azure, and GCP for data storage and processing. - Strong analytical and problem-solving skills to drive data quality and performance enhancements effectively. Preferred qualifications: - Familiarity with containerization tools like Docker and orchestration frameworks. - Knowledge of Big Data technologies such as Hadoop or Spark. - Experience working in Agile development environments and using CI/CD practices. Joining this dynamic work environment will offer opportunities for professional development and growth within the company. The inclusive culture focused on team success and respect for individual contributions makes it a rewarding place to work.,