Description

You will design, build, and maintain scalable data pipelines and distributed computing systems. You own the end-to-end data engineering lifecycle, from ingestion to optimization, ensuring high performance and reliability of big data infrastructure.

This role is on-site.

Responsibilities

• Develop and optimize large-scale data processing workflows using Apache Spark and Hadoop ecosystems.
• Build and maintain distributed data storage solutions using HDFS, Hive, and SQL-based query engines.
• Implement performance tuning strategies for data pipelines to ensure low-latency and high-throughput processing.
• Collaborate on data warehousing concepts and CI/CD pipeline implementation for data engineering projects.

Required Skills

• 5+ years of experience in big data technologies and distributed computing environments.
• Strong proficiency in Hadoop, Spark, Scala, Hive, HDFS, and SQL.
• Proven track record in data processing, distributed computing, and performance optimization.
• Solid understanding of core data engineering principles and architectural patterns.

Preferred Skills

• Experience with Kafka for real-time streaming, HBase, Airflow, or Oozie.
• Familiarity with cloud platforms (AWS, Azure, GCP) and Python programming.
• Experience with Impala for fast SQL queries and NoSQL database operations.

Key Skills

Education

Any Graduate