Source description
About the role
Data Engineer Exp - 8 years 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 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 hands-on expertise in Python, Spark, and Kafka Strong command of AWS or Azure services Strong hands-on capabilities on SQL and NoSQL technologies Sound understanding of data warehousing, modeling, and ETL concepts Familiarity with High-Level Design (HLD) and Low-Level Design (LLD) principles Excellent written and verbal communication skills Requirements
More at Awign Enterprises