Anudeep Madishetty

Spearheaded Python and SQL ETL pipeline development, utilizing Apache Spark and Hadoop, achieving a 30% reduction in data processing time, enhancing overall efficiency in data workflows.
Collaborated cross-functionally to elevate data modeling and validation processes, achieving a 15% increase in accuracy and consistency, empowering informed decision-making across teams.
Applied advanced statistical modeling techniques to conduct insightful analyses of large datasets, empowering data-driven decision-making and enhancing the organization's strategic initiatives.
Designed and implemented a robust microservices architecture on Kubernetes, enabling seamless communication between data-driven applications and enhancing system reliability, scalability, and performance.
Orchestrated the seamless deployment and management of Snowflake virtual warehouses, optimizing parallel processing for a 20% improvement in data operation efficiency, enhancing overall performance optimization and scalability.
Implemented Azure Data Factory (ADF) extensively for ingesting data from different source systems like relational and unstructured data to meet business functional requirements.
Design and developed Batch processing and Real-time processing solutions using ADF, Databricks clusters and stream Analytics and automated jobs using different triggers like Events, Schedules and Tumbling in ADF.
Created numerous pipelines in Azure using Azure Data Factory v2 to get the data from disparate source systems by using different Azure Activities like Move &Transform, Copy, filter, for each, Databricks etc.
Currently immersed in research, focusing on computer vision and data extraction from images using OpenCV and leveraged Natural Language Processing (NLP) to build models and train bots that learn from JIRA data, resulting in a 30% reduction in manual work and significantly improved accuracy.
Maintain and provide support for optimal pipelines, data flows and complex data transformations and manipulations using ADF and PySpark with Databricks.
Implemented robust security measures in PostgreSQL databases, incorporating encryption and accesscontrols, achieving a 40% enhancement in data protection and alignment with industry best practices for safeguarding sensitive information.
Conducted performance testing and benchmarking of HBase clusters, identifying bottlenecks and implementing optimizations for enhanced throughput.
Worked with complex SQL views, Stored Procedures, Triggers, and packages in large database management from various servers and used Azure DevOps pipelines to build and deploy different resources (Code and Infrastructure) in Azure.
Harnessed the capabilities of AzureDatabricks and HDInsight to propel advanced data processing, analytics, and machine learning initiatives. Utilized these services for scalable, efficient, and cutting-edge data-driven solutions.
Automated testing and deployment with Jenkins in CI/CD pipelines, streamlining the development process.
Integrated Apache Airflow into data workflows, automating intricate data processing tasks, resulting in a 25% boost in efficiency and a 30% reduction in manual intervention, streamlining operations.

• Spearheaded Python and SQL ETL pipeline development, utilizing Apache Spark and Hadoop, achieving a 30% reduction in data processing time, enhancing overall efficiency in data workflows.
• Collaborated cross-functionally to elevate data modeling and validation processes, achieving a 15% increase in accuracy and consistency, empowering informed decision-making across teams.
• Applied advanced statistical modeling techniques to conduct insightful analyses of large datasets, empowering data-driven decision-making and enhancing the organization's strategic initiatives.
• Designed and implemented a robust microservices architecture on Kubernetes, enabling seamless communication between data-driven applications and enhancing system reliability, scalability, and performance.
• Orchestrated the seamless deployment and management of Snowflake virtual warehouses, optimizing parallel processing for a 20% improvement in data operation efficiency, enhancing overall performance optimization and scalability.
• Implemented Azure Data Factory (ADF) extensively for ingesting data from different source systems like relational and unstructured data to meet business functional requirements.
• Design and developed Batch processing and Real-time processing solutions using ADF, Databricks clusters and stream Analytics and automated jobs using different triggers like Events, Schedules and Tumbling in ADF.
• Created numerous pipelines in Azure using Azure Data Factory v2 to get the data from disparate source systems by using different Azure Activities like Move &Transform, Copy, filter, for each, Databricks etc.
• Currently immersed in research, focusing on computer vision and data extraction from images using OpenCV and leveraged Natural Language Processing (NLP) to build models and train bots that learn from JIRA data, resulting in a 30% reduction in manual work and significantly improved accuracy.
• Maintain and provide support for optimal pipelines, data flows and complex data transformations and manipulations using ADF and PySpark with Databricks.
• Implemented robust security measures in PostgreSQL databases, incorporating encryption and accesscontrols, achieving a 40% enhancement in data protection and alignment with industry best practices for safeguarding sensitive information.
• Conducted performance testing and benchmarking of HBase clusters, identifying bottlenecks and implementing optimizations for enhanced throughput.

Developed and deployed deep learning models using TensorFlow, implemented neural networks for tasks such as image recognition, natural language processing, and predictive modeling.
Engineered and optimized highly efficient ETL pipelines with Apache Spark and Kafka, achieving a30% reduction in processing time. Ensured real-time data processing excellence, resulting in a 25% improvement in data availability.
Managed large-scale distributed data storage systems like Hadoop Distributed File System (HDFS) and NoSQL databases (e.g., MongoDB) to store and retrieve structured and unstructured data efficiently.
Architected and delivered transformative Power BI solutions, increasing decision-making efficiency by 20%. Integrated precise data modeling and ETLprocesses, ensuring a 15% improvement in meeting intricate business requirements.
Implemented automated data quality checks and validation processes to ensure the integrity and accuracy of incoming and stored data, reducing the likelihood of errors and ensuring high-quality outputs.
Excellent R computing skills for handling large datasets and complex computations.
Developed a Python script to monitor real-time storage on various Linux and Windows servers, logging errors and additional information, subsequently generating a report using the Matplotlib library to detail servers exceeding storage access.
Proficiently executed Hive queries and HQL on a high-performance computing (HPC) cluster to manage and analyze extensive datasets.
Designed and implemented a cloud-based data warehousing solution using Azure SQL Data Warehouse or Azure Synapse Analytics.
Developed ETL pipelines using Azure Data Factory to extract, transform, and load data from different sources into the data warehouse.
Designed and managed data warehousing solutions with Azure Synapse, integrating storage, analytics, and reporting for streamlined business intelligence. Architect and optimize solutions to enable efficient querying and reporting capabilities.
Leveraged Jenkins and Docker for automating testing, deploying, and versioning data engineering artifacts. Ensured reliability and reproducibility through robust version control and documentation, cultivating continuous improvement practices.
Executed data migration from HDFS to Azure SQL Data Warehouse by constructing ETL pipelines using Apache Spark. Employed diverse methods, including data fusion and machine learning, to enhance the accuracy of distinguishing pertinent rules from potential rules.

Designed and developed ETL processes in AWS Glue, migrating large volumes of campaign data from various sources to AWSRedshift, leveraging big data technologies.
Created and managed ETL pipelines, transforming and manipulating massive data sets usingAWS Glue, PySpark, and Databricks.
Extracted, aggregated, and consolidated Adobe data within AWS Glue using PySpark, enabling comprehensive analysis in a big data environment.
Implemented serverless architecture with AWS Lambda, S3, and DynamoDB for scalable data processing in a big data context.
Scheduled clusters and created Lambda functions for operational alerts, ensuring efficient management of big data workflows.
Utilized AWS services (EC2, IAM, S3, Lambda, EBS, ELB, Auto Scaling) for building and deploying data pipelines across systems.
Implemented Spark Streaming and RDD transformations for real-time analytics on mini batches of data in DataBricks.
Designed high-performance data ingestion pipelines using Apache Spark and Databricks Delta Lake, enabling efficient processing from multiple sources.
Collaborated with developers to create Tableaudashboards, visualizing and analyzing big data for actionable insights.
Expertise in working with analytics data, transforming and analyzing it as per requirements.
Proficient in SQL for creating and modifying database objects, optimizing queries using PySpark SQL.
Developed Spark applications in PySpark on distributed environments for efficient big data processing and proficient in distributed systems.
Solid experience in Data Warehousing best practices, following disciplined methodologies for managing big data projects.

Designed and developed scalable ETL pipelines in Python, achieving a 40% increase in data flow efficiency from source to destination systems, enhancing data processing speed and reliability.
Implemented and optimized algorithms in Python for data analysis, machine learning, and statistical modeling, showcasing proficiency in libraries such asNumPy, SciPy, and Scikit-Learn.
Contributed significantly to the design and maintenance of data warehouses, utilizing SQL to optimize data structures, resulting in a 25% improvement in analytical processing speed and more efficient reporting capabilities.
Conducted thorough capacity planning for MySQLdatabases, forecasting future data growth, and proactively scaled infrastructure to accommodate increased data volumes.
Engineered, developed, and maintained robust ETL pipelines, ensuring data quality, integrity, and reliability. Leveraged tools like ApacheKafka for real-time data streaming and AWS Glue for automated data extraction and transformation.
Applied advanced data analytical skills, including predictive and statistical modeling, resulting in the identification of key patterns and trends, contributing directly to a 15% improvement in decision-making accuracy.
Crafted comprehensive and visually appealing data visualizations using tools like Tableau andMS Excel. Generated diverse visualizations, including charts, graphs, and dashboards, to facilitate data-driven decision-making.
Utilized version control systems like Git/GitHub for code management, code review, collaborated with cross-functional teams using tools like Jira, and ensured effective communication in anAgile (SCRUM) or Waterfall project environment.
Orchestrated intricate data workflows using AWS Glue, Lambda, andStepFunctions, resulting in a20% reduction in processing time and improved overall system efficiency.
Expertly managed and optimized data storage on Amazon S3, implementing robust architectures and access controls. Executed strategic planning to ensure secure and scalable data handling, enhancing efficiency and reliability.
Translated intricate business requirements into effective data solutions through close collaboration with stakeholders, ensuring alignment with organizational objectives and driving impactful outcomes.
Excelled in full-stack web development with expertise in HTML, CSS, Bootstrap, JavaScript, React, Angular,Python,Nodejs, shiny environment, VBA for building dynamic and responsive data-driven applications.
Demonstrated proficiency in integrating data engineering solutions with enterprise systems such as SAP and IBMWebSphere (WAS), ensuring seamless interoperability and data flow.