Azure Databricks Data Engineer

2-3 years of experience in Data Engineering and Big Data technologies. Strong hands-on experience with Databricks. Proficiency in PySpark or Spark SQL for distributed data processing. Strong SQL skills with experience in query optimization and performance tuning. Experience in designing and developing ETL/ELT pipelines. Hands-on experience with Delta Lake, DataFrames, and Spark architecture. Knowledge of data warehousing concepts, dimensional modeling, and data lake architectures. Experience working with cloud platforms such as Azure, AWS, or GCP. Familiarity with Git-based source code management and DevOps practices. Strong analytical, debugging, and problem-solving skills.

Design, develop, and maintain scalable data pipelines using Databricks and Apache Spark. Build and optimize ETL/ELT workflows for processing large-scale structured and unstructured datasets. Develop data ingestion frameworks to integrate data from multiple sources, including databases, APIs, applications, and cloud storage. Implement and manage Lakehouse architecture using Databricks and Delta Lake. Optimize Spark jobs and Databricks workloads for performance, scalability, and cost efficiency. Collaborate with analytics, BI, and business teams to understand data requirements and deliver reusable data assets. Implement data quality frameworks, monitoring solutions, and governance controls across data pipelines. Troubleshoot production issues and provide timely resolution to ensure business continuity. Participate in architecture reviews, code reviews, and technical discussions to promote best practices. Create and maintain technical documentation, reusable components, and deployment standards.

Infosys Data & Analytics practice helps organizations modernize their data landscape through cloud-native architectures, advanced analytics, AI, and intelligent data platforms. As a Databricks Data Engineer, you will work on enterprise-scale data modernization programs involving Lakehouse implementations, cloud migrations, real-time analytics, and AI-ready data ecosystems.