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
