Senior Data Engineer
Current• Hands on experience in Python PySpark programming on Cloud era. Harton Works and MapR Hadoop Clusters. Aws EMR clusters, AWS Lambda functions and CFTS• Optimize Pyspark scripts to run on Palantir DEEP Env for faster data processing• Work closely with project Business Analyst. Data Modeler and BI Lead to ensure that the end to end designs meet the business and data requirements• Involved in story-driven Agile development methodology and actively participated in daily Scrum meetings• Conducted Data blending, Data preparation using Alteryx and SQL for Tableau consumption and publishing data sources to Tableau server.• Handle importing of data from various data sources; perform transformations using Hive, impala, Map Reduce, load data into HDFS and extract the data from Oracle into HDFS using Sqoop. • Experience in moving data between GCP and Azure using Azure data factory.• Design and Development of various data flows from different source systems to various cloud service providers using Python, Airflow and Apache Nifi• Launched multi-node kubernetes cluster in Google Kubernetes Engine (GKE) and migrated the dockerized application from AWS to GCP• Managed large datasets using Pandas and da - gcp package and MySQL • Worked with Hadoop, SSIS, SSAS OLAP cubes, Pentaho. Hadoop, PIG, Hive, Spark, Oracle, and MS SQL Server• Hands-on experience in using Google Stack driver for monitoring the logs of both GKE and GCP instances and configured alerts from Stack driver.• Develop the Spark Sql logics which mimic the Teradata ETL logics and point the output Delta back to Newly Created Hive Tables and as well the existing TERADATA Dimensions, Facts, and Aggregated Tables.• Setting Up AWS and Microsoft Azure with Databricks, Databricks Workspace for Business Analytics, Manage Clusters in Databricks