Helping Customers to Build Scalable ETL, EL-T pipelines and implement several Data Cleansing Transformation processes, having around 6+ years of experience in Data Engineering, AWS, Microservice, and API development, and in various Big Data Tools and Technologies. ********************************************* Objective: Looking for a role to apply my Engineering Skills which will benefit my client and also nourish my knowledge ********************************************* I love new challenges like learning new technologies and don't follow the same path of solving a problem. Currently, I am learning about Data Minning and Feature Engineering. +++++++++ SUMMARY +++++++++++ • Profile: Data Engineer, Cloud App Engineer • Cloud Platform Known: AWS • DataBase knew: Oracle, MySQL, DynamoDB, MongoDB, Neo4J. • Data-Warehousing: Redshift, Hive (HiveQL ), Google BigQuery • Data - Visualization: Plotly, Seaborn (Send <div> payload via REST Reply and Custom Dashboarding Web Page show the respective plot), Google Data Studio. • Scheduler: Cron Job, Airflow • Big Data Framework: Apache Spark , Apache Tez • Streaming Technology: Kafka, AWS Kineses • Programming Language known: Scala , Python, SQL , Golang • ETL service: AWS Glue • Workflow Service: AWS Step Function • Version Control: Git • Deployment and Packaging: Docker • Testing (TDD) : PyTest , ScalaTest . • Community: Stack-overflow, Github • AWS Services known: Fargate, Kinesis, Lambda, CFn, DynamoDB, API Gateway, Cognito, Step Function, SQS, SAM