Job Description

Responsibilities :

Design, develop, and optimize large-scale data pipelines using Azure Databricks and PySpark. Build and maintain ETL/ELT workflows using Azure Data Factory. Develop scalable data processing frameworks for batch and near real-time data ingestion. Implement data transformation, cleansing, and enrichment processes. Work with large-scale structured and unstructured datasets in Azure Data Lake. Optimize Spark jobs for performance, scalability, and cost efficiency. Collaborate with business stakeholders, architects, and analytics teams to deliver data solutions. Ensure data quality, security, governance, and compliance standards are met. Provide production support, troubleshoot issues, and drive continuous improvements. Mentor junior team members and contribute to technical solution design.

Technical and Professional Requirements:

12-16 years of IT experience with strong Data Engineering expertise. Hands-on experience in Azure Databricks and PySpark development. Strong knowledge of Azure Data Factory (ADF). Expertise in SQL and Data Warehousing concepts. Experience with Azure Data Lake Storage (ADLS Gen2). Strong understanding of Spark architecture and performance tuning. Experience designing and implementing enterprise-scale data pipelines. Knowledge of CI/CD and Agile delivery practices.