Responsibilities:

Build and optimize large-scale data pipelines using Python and Apache Spark to support reliable data processing.
Develop ETL workflows that collect, transform, and deliver data from multiple source systems into analytical platforms.
Work with Hadoop-based environments to manage distributed data processing and storage effectively.
Implement and support Kafka-driven streaming solutions for near real-time data ingestion and integration.
Monitor pipeline performance, troubleshoot data issues, and improve processing efficiency across engineering workflows.
Collaborate with technical teams to understand data needs and translate them into scalable engineering solutions.
Maintain data quality standards by validating outputs, resolving inconsistencies, and supporting dependable data availability.
Contribute to ongoing enhancements of data architecture, including updates to existing platforms and operational processes. Requirements
Hands-on experience with Apache Spark for large-volume data processing.
Strong programming ability in Python for data engineering and automation tasks.
Working knowledge of Apache Hadoop and distributed data ecosystems.
Experience using Apache Kafka for event-driven or streaming data solutions.
Proven background in ETL design, development, and support.
Ability to troubleshoot data pipeline issues and improve performance in complex environments.
Strong collaboration and communication skills in cross-functional technical settings.

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