Big Data Engineer

Summary

Join Nagarro, a rapidly growing digital product engineering company, as a Data Engineer. You will develop comprehensive technical plans for data migration within Azure's Data Factory and data lake. Responsibilities include designing and implementing scalable data pipelines using Azure Databricks, developing reusable data ingestion frameworks, and ensuring data quality. You will provide technical guidance to the team and stay current with advancements in cloud computing and data engineering. This role requires 4-8 years of experience in data engineering and expertise in Azure Databricks, data modeling, PySpark, and SQL.

Requirements

• 4 to 8 years of experience in Data Engineering or similar roles
• Experience working with Azure Databricks
• Expertise in Data Modeling and Source System Analysis
• Proficiency with PySpark
• Mastery of SQL
• Knowledge of Azure components: Azure Data Factory, Azure Data Lake, Azure SQL DW, and Azure SQL
• Experience with Python programming for data engineering purposes
• Ability to conduct data profiling, cataloging, and mapping for technical design and construction of technical data flows
• Experience with data visualization and exploration tools

Responsibilities

• Develop a comprehensive technical plan for the migration, including data ingestion, transformation, storage, and access control within Azure’s Data Factory and data lake
• Design and implement scalable and efficient data pipelines to ensure smooth data movement from multiple sources using Azure Databricks
• Develop scalable and reusable frameworks for data ingestion
• Ensure data quality and integrity throughout the data pipeline, implementing robust data validation and cleansing mechanisms
• Work with event-based and streaming technologies to ingest and process data
• Provide technical guidance and support to the team, resolving technical challenges or issues during the migration and post-migration phases
• Stay current with advancements in cloud computing, data engineering, and analytics technologies, recommending best practices and industry standards for implementing data lake solutions