Data Engineering Developer - Assistant Manager

Role Summary We are seeking a highly skilled Data Engineer with strong expertise in Databricks, Apache Spark, and Scala to design, develop, and optimize large-scale data processing solutions. The ideal candidate will have hands-on experience building distributed data pipelines, implementing scalable ETL frameworks, and delivering cloud-native data solutions on Azure. Key Responsibilities Design, develop, and maintain scalable data pipelines using Databricks, Apache Spark, and Scala. Build ingestion, transformation, and validation frameworks for high-volume data processing. Develop and optimize Spark jobs for performance, scalability, and reliability. Implement and manage Databricks workflows, job orchestration, and scheduling. Troubleshoot production issues, performance bottlenecks, and pipeline failures. Optimize cluster utilization, Spark execution plans, and data processing efficiency. Work with cross-functional teams to design data architectures and integration solutions. Implement best practices for coding, testing, CI/CD, and deployment. Support production environments and participate in incident resolution and root cause analysis. Collaborate in Agile teams to deliver high-quality data products. Required Skills Strong hands-on experience in: Databricks Apache Spark Scala Spark SQL PySpark Experience in: Distributed data processing frameworks ETL/ELT pipeline development Data modeling and data warehousing concepts Performance tuning and optimization of Spark applications Cloud experience: Microsoft Azure Azure Data Lake Storage (ADLS) Azure Data Factory (ADF) Strong SQL development and query optimization skills. Experience with source control systems such as Git/GitHub. Strong analytical and debugging skills. Preferred Skills Delta Lake Unity Catalog CI/CD pipelines (Azure DevOps, Harness) Snowflake Data Quality and Validation Frameworks Financial Services / Reference Data domain knowledge Experience with AI-assisted development tools such as GitHub Copilot Education & Experience Bachelor's or Master's degree in Computer Science, Engineering, or related field. 10+ years of experience in Data Engineering. 6+ years of hands-on Databricks and Spark development experience. Strong experience developing production-grade Scala applications. Nice-to-Have Databricks Certification Azure Certification Experience with Real-Time Streaming (Kafka, Spark Structured Streaming) Exposure to Lakehouse architecture patterns