Manikumar D Email & Phone Number

United States

 Developed and optimized end-to-end data pipelines using Hadoop, Spark, Hive, and HBase, handling large volumes of data from multiple sources to support real-time and batch processing. Created complex MapReduce jobs and Hive queries to handle data transformations and aggregations that could not be addressed by standard tools, enhancing the data processing capabilities. Developed streaming data pipelines using Apache Kafka, Apache Spark Streaming, and Azure Stream Analytics, enabling real-time analytics and anomaly detection for financial and e-commerce clients. Utilized Azure services (Azure Data Lake Storage, Azure Synapse Analytics, Azure HDInsight) and on-premises big data tools (Hadoop, HBase, Hive) to build scalable and efficient data architectures for various projects. Collaborated with data scientists to build and deploy machine learning models for predictive analytics using Azure Machine Learning, Spark MLlib, and Azure Databricks, enhancing business insights and decision-making. Designed and implemented ETL processes using Azure Data Factory and Apache Airflow, automating data ingestion, transformation, and loading tasks to ensure data consistency and reduce manual intervention.

Bengaluru, Karnataka, India

 Engineered and optimized end-to-end ETL pipelines using Apache Spark, Python, and Apache Kafka to handle large volumes of data from multiple sources, ensuring seamless data ingestion, processing, and storage. Implemented real-time data streaming solutions using Apache Flink and Google Dataflow for healthcare and retail clients, enabling real-time analytics, predictive modeling, and faster decision-making. Utilized Azure to build scalable and efficient data architectures, including data lakes, data warehouses, and real-time analytics platforms, ensuring high availability and performance. Deployed and managed data storage solutions using Azure Blob, optimizing data retrieval and analytics performance by designing efficient schemas and indexing strategies. Automated complex ETL workflows using Apache Airflow, reducing data processing time by 40% and minimizing manual intervention for data validation and transformation. Deployed and managed containerized data pipelines and machine learning models on Kubernetes (GKE) and Docker, ensuring scalability, resilience, and high availability in production environments. Implemented data security measures using Azure Active Directory to manage sensitive information and credentials, ensuring compliance with industry regulations like GDPR and PCI-DSS.

Master Of Science - Ms, Major In Data Science & Analytics

Bachelor Of Technology - Btech