Job descriptionData Pipeline Development : Design, implement, and manage scalable ETL / ELT pipelines using AWS services and Databricks.Data Integration : Ingest and process structured, semi-structured, and unstructured data from multiple sources into AWS Data Lake or Databricks.Data Transformation : Develop advanced data processing workflows using PySpark, Databricks SQL, or Scala to enable analytics and reporting.Databricks Management : Configure and optimize Databricks clusters, notebooks, and jobs for performance and cost efficiency.AWS Architecture : Design and implement solutions leveraging AWS-native services like S3, Glue, Redshift, EMR, Lambda, Kinesis, and Athena.Collaboration : Work closely with Data Analysts, Data Scientists, and other Engineers to understand business requirements and deliver data-driven solutions.Performance Tuning : Optimize data pipelines, storage, and queries for performance, scalability, and reliability.Monitoring and Security : Ensure data pipelines are secure, robust, and monitored using CloudWatch, Datadog, or equivalent tools.Documentation : Maintain clear and concise documentation for data pipelines, workflows, and architecture