Prashanth Dev

Background

About Prashanth Dev

Experienced Data Engineer with over 12 years of expertise in database design and development, data modeling, ETL processes, and end-to-end data architecture across both on-premise and cloud environments. I have designed and implemented scalable ETL pipelines, developed complex data flow diagrams, ensured high standards of data quality, and optimized performance in OLAP and OLTP systems. My background includes deep experience with report automation, SQL reporting, normalization techniques, multidimensional databases, and KPIs. I have led successful migrations of on-premises SQL databases to Azure platforms such as Azure Data Lake, Azure Data Lake Analytics, Azure SQL Database, and Azure Synapse using tools like Azure Data Factory and Databricks, while managing secure access controls and transformation logic. I bring strong hands-on experience with big data technologies including Apache Spark, Azure Databricks, HDInsight, Hive, HDFS, and Azure Functions, and have worked extensively within the Cloudera ecosystem using tools such as HDFS, YARN, Hive, Sqoop, Flume, HBase, Oozie, Kafka, Pig, Impala, and Spark SQL. My data pipeline orchestration skills are supported by deep experience in Apache Airflow and Python scripting. I have implemented automated CI/CD pipelines using Jenkins for deploying micro services on AWS ECS, Python-based jobs on AWS Lambda, and containerized Java and Python applications. My data integration and warehousing expertise includes building complex SSIS and DTS packages, and I’m proficient in data warehouse architecture and dimensional modeling using tools like Erwin and ER/Studio, including star and snowflake schema design. I am well-versed in AWS services including EC2, S3, Redshift, IAM, Kinesis, CloudFormation, CloudWatch, Lambda, CodePipeline, and more, and also have hands-on experience in monitoring and analytics using the ELK Stack—Elasticsearch, Logstash, and Kibana. Additionally, I have integrated and worked with NoSQL databases such as DynamoDB, Cosmos DB, MongoDB, Cassandra, and HBase. My expertise spans across multiple cloud platforms and big data tools, allowing me to deliver reliable, efficient, and scalable data solutions that support robust analytics and business intelligence initiatives.