Data Engineer for Scalable Systems
5 days ago
This is a full-time hybrid role that involves building and maintaining large-scale data infrastructures, developing real-time data processing pipelines using technologies like Kafka, Flink, and Spark. The candidate will be responsible for designing and implementing data models, ETL processes, and ensuring data quality and efficiency.
Key Responsibilities:- Data Processing System Design: Develop scalable data processing systems that can handle high volumes of data in real-time.
- Real-time Data Processing Pipelines: Implement data pipelines using Kafka, Flink, and Spark to process and analyze large datasets.
- Data Quality and Efficiency: Ensure data quality and efficiency through effective data modeling and ETL processes.
- Data Engineering Skills: Proficiency in data engineering, including experience with Kafka, Flink, and Spark.
- Data Modeling Expertise: Strong skills in data modeling and design.
- ETL Processes: Experience with Extract Transform Load (ETL) processes and data warehousing.
- Data Analytics: Strong skills in data analytics and problem-solving.
The ideal candidate will have excellent communication and teamwork abilities, and the ability to work in a hybrid environment, balancing in-office and remote work.
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