Design, develop, test, and deploy high-performance Apache Spark/Scala data processing applications and ETL pipelines on Cloudera (CDH). Optimize Spark and platform performance, build data pipelines using HDFS, Hive, Impala, HBase, and Kafka, ensure data integrity and security, collaborate with data scientists and analysts, and implement version control and CI/CD for deployments.
Key Responsibilities:
- Develop, test, and deploy data processing applications using Apache Spark and Scala.
- Optimize and tune Spark applications for better performance on large-scale data sets.
- Work with the Cloudera Hadoop ecosystem (e.g., HDFS, Hive, Impala, HBase, Kafka) to build data pipelines and storage solutions.
- Collaborate with data scientists, business analysts, and other developers to understand data requirements and deliver solutions.
- Design and implement high-performance data processing and analytics solutions.
- Ensure data integrity, accuracy, and security across all processing tasks.
- Troubleshoot and resolve performance issues in Spark, Cloudera, and related technologies.
- Implement version control and CI/CD pipelines for Spark applications.
Required Skills & Experience:
- Minimum 8 years of experience in application development.
- Strong hands on experience in Apache Spark, Scala, and Spark SQL for distributed data processing.
- Hands-on experience with Cloudera Hadoop (CDH) components such as HDFS, Hive, Impala, HBase, Kafka, and Sqoop.
- Familiarity with other Big Data technologies, including Apache Kafka, Flume, Oozie, and Nifi.
- Experience building and optimizing ETL pipelines using Spark and working with structured and unstructured data.
- Experience with SQL and NoSQL databases such as HBase, Hive, and PostgreSQL.
- Knowledge of data warehousing concepts, dimensional modeling, and data lakes.
- Ability to troubleshoot and optimize Spark and Cloudera platform performance.
- Familiarity with version control tools like Git and CI/CD tools (e.g., Jenkins, GitLab).
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