Shape the future of data by designing, developing, and maintaining robust data solutions. This role focuses on creating efficient data generation, collection, and processing systems. You will build essential data pipelines, ensure the highest data quality standards, and implement sophisticated ETL processes for seamless data migration and deployment across various systems. Experience in Python frameworks is crucial for success in this dynamic position.

Architect and build highly scalable data pipelines and ETL/ELT processes for large-scale data handling. Develop and maintain data services and APIs to facilitate smooth data consumption across systems. Guarantee data integrity, validation, and consistency throughout all pipelines. Optimize data workflows for enhanced scalability, performance, and cost-effectiveness. Efficiently manage high-volume and high-velocity datasets. Mentor junior team members, fostering their professional development and growth. Continuously refine data engineering methodologies to boost team productivity and efficiency.

A strong foundation in Python is essential, coupled with significant experience in building scalable data pipelines and distributed data processing systems. A solid grasp of databases (SQL and NoSQL) and advanced query optimization techniques is required. Proficiency in ETL/ELT frameworks and data integration patterns is key. Hands-on experience with major cloud platforms (AWS, Azure, or Google Cloud) and their native data services is necessary. Familiarity with workflow orchestration tools like Apache Airflow and CI/CD tools such as Git and Jenkins, along with containerization using Docker, is beneficial. A deep understanding of performance tuning, scalability, and distributed system design principles is vital. Knowledge of data quality, lineage, and governance frameworks is important. A minimum of 5 years of experience in Python programming is required, alongside 15 years of full-time education.