Description

You will own the design, development, and deployment of scalable distributed data processing and replication strategies, while mentoring developers on architectural guidance.

This role is on-site.

Responsibilities

• Design and implement scalable data ingestion and transformation pipelines using Java, Spark, Python, and Scala.
• Analyze complex systems and collaborate on data-driven product designs from development through production deployment.
• Perform large-scale data analysis using Hadoop ecosystem components including HDFS, HBase, and Hive.
• Evaluate emerging technologies and prototype solutions to enhance data processing architecture.
• Ensure adherence to secure application development standards and automated QA practices.

Required Skills

• 5+ years of experience delivering enterprise software solutions with significant big data exposure.
• Strong hands-on expertise in Java, Apache Spark, Scala, and Python for distributed applications.
• 3+ years working with Hadoop and Spark ecosystem technologies (MapReduce, HDFS, HBase, Hive, Flume, Sqoop, Kafka).
• Proven experience with AWS Cloud technologies and implementing scalable cloud solutions.
• Deep understanding of data modelling, schema design, and optimization techniques.
• Experience with object-oriented design, Agile development, and automated QA processes.
• Bachelor’s degree in Computer Science, Computer Engineering, or equivalent.

Preferred Skills

• Hands-on experience with Apache Airflow and Python-based workflow orchestration.
• Ability to translate business requirements into effective technical implementations quickly.

Key Skills

Education

Any Graduate