Data Engineer

Roles and Responsibilities

• Develop and manage robust ETL pipelines using Apache Spark (Scala)
  
• Understand Spark concepts, performance optimization techniques, and governance tools
  
• Develop a highly scalable, reliable, and high-performance data processing pipeline to extract, transform, and load data from various systems to the Enterprise Data Warehouse/Data Lake/Data Mesh hosted on AWS or Azure
  
• Collaborate cross-functionally to design effective data solutions
  
• Implement data workflows utilizing AWS Step Functions or Azure Logic Apps for efficient orchestration. Leverage AWS Glue and Crawler or Azure Data Factory and Data Catalog for seamless data cataloging and automation
  
• Monitor, troubleshoot, and optimize pipeline performance and data quality
  
• Maintain high coding standards and produce thorough documentation. Contribute to high-level (HLD) and low-level (LLD) design discussions
  
  Technical Skills
  ● Minimum 2 years of progressive experience building solutions in Big Data environments
  ● Have a strong ability to build robust and resilient data pipelines which are scalable, fault tolerant, and reliable in terms of data movement
  ● 2+ years of hands-on expertise in Python, Spark, and Kafka
  ● Strong command of AWS or Azure services
  ● Strong hands-on capabilities on SQL and NoSQL technologies