Ashwik Gadikindi Email & Phone Number

United States

Ohio, United States

> Designed and implemented an Enterprise Data Lake to support various use cases, including analytics, processing, storage, and reporting of large, rapidly changing datasets, improving data accessibility and scalability.> Developed real-time streaming applications using PySpark, Apache Flink, Kafka, and Hive on distributed Hadoop clusters, allowing for efficient handling of large-scale streaming data.> Created a security framework for fine-grained access control to AWS S3 objects using AWS Lambda and DynamoDB, ensuring that sensitive data was securely stored and accessed only by authorized users.> Managed complex data migration projects, including migrating from Oracle 10g RAC to Oracle 11g RAC, which involved writing complex SQL queries and developing PL/SQL packages and scripts to ensure data integrity and seamless transitions.> Migrated on-premises ETL processes to Google Cloud Platform (GCP) using tools like BigQuery, DataProc, Google Cloud Storage, and Composer, enhancing the organization’s ability to leverage cloud resources for data processing and storage.

Missouri, United States

>Developing scalable and efficient data pipelines. This involved using programming languages and technologies such as Python, SQL, and cloud-based tools to handle large volumes of data across different systems.>I was responsible for developing and maintaining data warehousing solutions, ensuring that large volumes of healthcare data were stored, managed, and retrieved efficiently to support analytics and reporting requirements.>I also played a key role in integrating data processing workflows with cloud platforms, utilizing cloud resources to ensure scalability, flexibility, and performance. This helped in managing and analyzing large datasets effectively.>Worked closely with data scientists, analysts, and other stakeholders to understand their requirements and also have ability to translate business needs into technical specifications ensured that data solutions aligned with organizational objectives.>Optimized ETL processes to enhance data quality and reduce processing times and my work also includes implementing best practices for data transformation and load procedures, which contributed to more efficient data handling.

Michigan, United States

>I was responsible for installing, configuring, and maintaining data pipelines. This included creating and managing data pipelines using Azure Data Factory (ADF) to extract, transform, and load data from various sources like Azure SQL, Blob Storage, and Azure SQL Data Warehouse.>Managed data extraction processes from Hadoop to Amazon S3 on a daily or hourly basis, which indicates my experience in integrating on-premises big data platforms with cloud storage solutions.>Involved in managing Azure Data Lakes and utilizing Data Lake Analytics and also my role included integrating with other Azure services, ensuring efficient data storage and analysis.>I also worked closely with Data Governance and Data Quality teams to design effective data models and processes, ensuring data integrity and compliance with governance standards.>Developed data visualizations using tools like Tableau, and D3, supporting data-driven decision-making by presenting insights in an accessible and interpretable manner.

India

>Responsible for designing, implementing, and managing AWS cloud infrastructure and my work included setting up and optimizing cloud resources like EC2, S3, Lambda, and RDS to support scalable and secure data processing environments.>Developed and maintained data pipelines for data ingestion, transformation, and storage using AWS services such as AWS Glue, AWS Lambda, and Amazon Redshift. These pipelines facilitated the efficient movement and processing of large datasets.>I also have good experience in serverless technologies, particularly AWS Lambda, to build event-driven architectures that reduced operational overhead and improved scalability and reliability of data processing workflows.> I was involved in setting up and managing data lakes on AWS, utilizing S3 for storage and Glue Catalog for metadata management which allowed for scalable storage solutions and efficient data retrieval.>By using scripting languages like Python and leveraging AWS CloudFormation, he automated infrastructure provisioning and data workflows, resulting in optimized performance and reduced costs.

India

>Designed and implemented data analytics solutions within the Hadoop ecosystem using Apache Spark, Hive, Pig, Sqoop, HBase, Oozie, Impala, and Kafka.>Built Oozie workflows for ETL processes, including data import/export between Teradata/SQL and Hive, and performed data aggregation based on business requirements.>Managed and optimized Apache Hadoop clusters, including CDH, MapReduce, and Amazon EMR on EC2, ensuring efficient data processing and scalability.>Developed data mapping, transformation, and cleansing rules for managing OLTP and OLAP systems, and implemented UNIX shell scripts for table maintenance tasks.> Utilized Git for version control and managed CI/CD pipelines using tools like Screwdriver.

Bachelor Of Technology In Computer Science

Master’S Degree, Computer Science