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Job description
We are seeking an experienced Kafka/Spark Data Engineer with strong expertise in real-time data processing, streaming technologies, and distributed systems. The role will focus on Kafka, Spark, Redis, Python, and cloud-based Big Data solutions.
Roles and Responsibilities
Design, develop, and support real-time data streaming and Big Data solutions.
Develop streaming applications using Apache Kafka, KStreams, and KTables.
Work with Apache Spark for large-scale data processing.
Develop applications using Python, Scala, Java, and Bash/Shell scripting.
Work with NoSQL databases such as Redis, MongoDB, and HBase.
Design scalable, distributed, and fault-tolerant data architectures.
Implement data integration, security, and authentication solutions, including Kerberos.
Work with streaming technologies such as Flink or Storm.
Support cloud-based data solutions across AWS, Azure, or GCP.
Apply data analysis, statistical methods, and machine learning techniques where required.
Create and support data visualizations using Tableau.
Troubleshoot business-critical applications and provide effective technical solutions.
Required Qualifications
9+ years of overall IT/industry experience, with strong Big Data and real-time processing experience.
Bachelor's or Master's degree in Computer Science, Engineering, Science, or related field.
Strong hands-on experience with Kafka and real-time streaming.
Experience with Spark, Redis, and Python.
Strong understanding of distributed systems, data partitioning, and fault-tolerant architectures.
Experience with NoSQL databases and cloud platforms.
Strong problem-solving, analytical, and communication skills.
Preferred Qualifications
Experience with Flink, Storm, Scala, or Java.
Knowledge of machine learning and predictive analytics.
Experience with Tableau and real-time data visualization.
Who can apply
Review the original listing for work authorization, qualifications and employer requirements.