Rajesh Doolla

Background

About Rajesh Doolla

Passionate and Experienced Data Engineer, Machine Learning & Data Analyst with a demonstrated history of working in the Travel & leisure, Insurance, Healthcare, Telecommunications, and Resources & Chemical industry. Skilled in Data engineering, Machine learning, and Data Visualization. With a strong background in Data Modernization, Data and statistical modelling. Love working on data problems and have a keen interest in finding patterns out of them. Handled Client facing roles in global geographics like USA, UK, Hungary, Argentina, and Brazil. Highly experience in batch & real-time application design & development using Python/Scala. Experienced in big data components both on-premises and cloud platforms; Hadoop, Hive, Sqoop, HBase, Kafka, PySpark, Airflow, AWS EMR, S3, Redshift, Athena, Data pipeline, Databricks. Worked on orchestration tools like airflow schedule the data pipelines. I have extensive knowledge on ops work like CI/CD- Jenkins, GitHub actions, Containerization: Docker & Kubernetes, monitoring & Loging: Datadog, Splunk. Experienced in developing, architecting, and managing big data pipelines/ETL along with reporting by data analysis and implementation of machine learning algorithms to provide action-oriented solutions to complex business problems Experience in developing and architecting several batch data pipelines on HDFS on-premises and on AWS cloud or using Databricks, by considering optimal cost, fault-tolerant, and monitoring capability. Experience in designing and developing the ETL/data pipelines for Machine Learning models to operationalize the models with cost-effective and low latency systems using Databricks. Experienced in big data components both on-premises and cloud platforms; Hadoop, Hive, Sqoop, HBase, Kafka, PySpark; AWS- AWS EMR, S3, Dynomo DB, Redshift, Athena, EKS, Databricks, GCP – Big Query, Dataproc and Dataflow. Extensive work experience in Both AWS & GCP Data Engineer Components. Working knowledge on Implementing the Delta Live tables framework in Databricks.