Responsibilities:

Design, develop, and maintain scalable data pipelines using Apache Spark.
Write clean, efficient, and reusable code in Java or Scala (strong preference for Java/Scala).
Work with HBase and Hive for data storage, processing, and retrieval in a distributed environment.
Optimize queries and data operations using advanced SQL techniques.
Ensure high availability and scalability of distributed data processing systems.
Collaborate with cross-functional engineering teams to integrate data systems with microservices.
Implement best practices in data engineering and ensure data quality, integrity, and consistency.
Work in Unix/Linux environments to manage data infrastructure and automation.
Participate in system architecture design discussions and recommend solutions to improve data platform efficiency.
Provide support during development, testing, deployment, and operational phases of data solutions.

Required Skills & Qualifications:

Bachelor's degree in Computer Science, Information Technology, Engineering, or related field
7–9 years of experience in data engineering or backend systems development.
Strong hands-on experience with Apache Spark.
Proficiency in Java or Scala (mandatory – Python developers will not be considered).
Experience with HBase and Hive in production environments.
Solid understanding of SQL and relational database systems.
In-depth knowledge of distributed systems architecture and scalable system design.
Experience working in Unix/Linux environments.
Familiarity with the Spring Framework and microservices-based architecture.
Strong problem-solving, debugging, and analytical skills.
Excellent communication skills and ability to work in collaborative, cross-functional teams
If required, must be able to work on weekend or public holidays for deployment to production server and cut over activities

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