Amol Z.

As a Senior Data Engineer with 9 years of extensive experience in the Data Engineering with Python ,Spark, Databricks, ETL Pipelines, Azure and AWS services, develop PySpark scripts and store data in ADLS using Azure Databricks. Additionally, I have created data pipelines for reading streaming data from MongoDB and developed Neo4j graphs based on stream-based data. I am well-versed in designing and modeling databases using Neo4j and MongoDB. I am seeking a challenging opportunity in a dynamic organization that can enhance my personal and professional growth while enabling me to make valuable contributions towards achieving the company's objectives. • Utilizing Azure Databricks to develop PySpark scripts and store data in ADLS. • Developing producers and consumers for stream-based data using Azure Event Hub. • Designing and modeling databases using Neo4j and MongoDB. • Creating data pipelines for reading streaming data from MongoDB. • Creating Neo4j graphs based on stream-based data. • Visualizing data for supply-demand analysis using Power BI. • Developing data pipelines on Azure to integrate Spark notebooks. • Developing ADF pipelines for a multi-environment and multi-tenant application. • Utilizing ADLS and Blob storage to store and retrieve data. • Proficient in Spark, HDFS, Hive, Python, PySpark, Kafka, SQL, Databricks, and Azure, AWS technologies. • Utilizing AWS EMR clusters to execute Hadoop ecosystems such as HDFS, Spark, and Hive. • Experienced in using AWS DynamoDB for data storage and caching data on Elasticache. • Involved in data migration projects that move data from SQL and Oracle to AWS S3 or Azure storage. • Skilled in designing and deploying dynamically scalable, fault-tolerant, and highly available applications on the AWS cloud. • Executed transformations using Spark, MapReduce, loaded data into HDFS, and utilized Sqoop to extract data from SQL into HDFS. • Proficient in working with Azure Data Factory, Azure Data Lake, Azure Databricks, Python, Spark, and PySpark. • Implemented a cognitive model for telecom data using NLP and Kafka cluster. • Competent in big data processing utilizing Hadoop, MapReduce, and HDFS.