As a Data Platform Engineer, you will contribute to the blueprint and design of the data platform, ensuring seamless integration across systems and data models. Your role will be central to shaping and enhancing our data platform components.

This position offers a dynamic environment where you'll collaborate closely with Integration Architects and Data Architects to achieve cohesive system and data model integration. You will play a key part in developing and maintaining the infrastructure that powers our data initiatives.

- Build, maintain, and optimize scalable data pipelines for large-scale data processing. - Develop and implement ETL/ELT processes using PySpark and Spark. - Assist in designing and deploying solutions on cloud platforms like AWS, Azure, or GCP. - Support Big Data processing frameworks and data lakes for diverse data types. - Ensure data accuracy and availability through collaboration with cross-functional teams. - Implement robust data ingestion strategies for secure and efficient data movement. - Work with real-time streaming and batch data processing to handle high-volume workloads. - Develop reusable code for ETL operations. - Contribute to performance tuning of Spark jobs and data pipelines for scalability. - Assist in maintaining data governance and security practices across cloud environments.

- Experience with AWS, Azure, or GCP for data engineering workflows. - Strong proficiency in PySpark or Spark for building scalable data pipelines. - Understanding of Big Data architectures, data storage, and processing concepts. - Familiarity with cloud-native data storage solutions (e.g., S3, Blob Storage). - Experience with data orchestration tools such as Apache Airflow. - Knowledge of data formats like Parquet or Avro. - Strong Python coding skills for data pipeline development. - Good understanding of SQL and database technologies. - Excellent troubleshooting, debugging, and performance optimization skills.

- 15 years of full-time education is required. - Minimum 3 years of experience is mandatory.