Java Developer with Big Data Expertise
2 days ago
Client is a company that helps other businesses with technology and consulting services. They offer various services like developing software, managing computer systems, and providing advice on how to improve business processes.
Overall, Client is like a trusted partner that businesses can rely on to solve their technology-related challenges and make their operations better. They have experts who understand the business needs and use technology to find the right solutions.
The ideal candidate will have a strong background in Java development and experience working with big data technologies such as Hadoop and Spark. The role involves designing, developing, and maintaining scalable data processing applications using Java and Apache Spark. The successful candidate will collaborate with data engineers and analysts to create robust data pipelines and work with big data technologies such as Hadoop, Spark, Kafka, and NoSQL databases.
The salary range for this position is estimated at $65-$70/Hr.
Job Description:
Responsibilities:
- Development & Design:Design, develop, and maintain scalable data processing applications using Java and Apache Spark.
- Big Data Technologies: Work with big data technologies such as Hadoop, Spark, Kafka, and NoSQL databases.Optimize data storage and retrieval processes to ensure high performance.
- Code Quality & Maintenance:Write clean, maintainable, and efficient code while following best practices.Conduct code reviews and provide constructive feedback.
- Troubleshooting & Support: Diagnose and resolve performance issues and bugs.Provide technical support and guidance to team members.
- Documentation: Maintain documentation for code, processes, and best practices.
- Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
- 3+ years of experience in Java development.
- 2+ years of experience with Big Data technologies, specifically Apache Spark.
- Proficient in Java and related frameworks (Spring, Hibernate, etc.).
- Strong understanding of Spark architecture and core concepts.
- Experience with Hadoop ecosystem components (HDFS, YARN, Hive, etc.).
- Familiarity with SQL and NoSQL databases (e.g., MongoDB, Cassandra).
- Knowledge of data processing and ETL frameworks.
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