Advance your career as a Data Engineer by designing, developing, and maintaining robust data solutions. This role focuses on the generation, collection, and processing of data, creating efficient data pipelines, and ensuring high data quality through ETL processes.

Key responsibilities include building and optimizing scalable data pipelines, backend data services, and cloud-based solutions. You'll collaborate with cross-functional teams to manage large volumes of data, ensuring performance, reliability, and data integrity across multiple systems. A cloud-first, agile approach is essential for delivering enterprise-grade data solutions.

Design and develop highly scalable data pipelines and ETL/ELT processes for large-scale data processing. Build and maintain data services/APIs for seamless data consumption across systems. Ensure data quality, validation, and consistency across pipelines. Optimize data workflows for scalability, performance, and cost efficiency. Handle high-volume and high-velocity datasets with efficient processing strategies. Mentor junior team members and contribute to team productivity improvements. Provide solutions to immediate and cross-team challenges, contributing to key decisions.

A minimum of 5 years of experience in Python programming is essential. Strong experience in building scalable data pipelines and distributed data processing systems. Solid understanding of both SQL and NoSQL databases, including advanced query optimization. Proficiency with ETL/ELT frameworks and data integration patterns. Hands-on experience with multi-cloud platforms (AWS, Azure, or Google Cloud) and their data services. Familiarity with workflow orchestration tools like Apache Airflow. Experience with CI/CD tools (Git, Jenkins) and containerization (Docker). Strong grasp of performance tuning, scalability, and distributed system design principles.