Bharath Kumar Polisetty Email & Phone Number

Collaborated with cross-functional teams to implement SDLC best practices, enhancing the development and deployment of scalable data pipelines. Optimized workflows with AWS Glue, reducing processing times by 25% through automated metadata management. Designed and deployed pipelines using Apache Spark, Hadoop, and MapReduce, improving data handling capacity by 40%.Integrated AWS RDS and Amazon S3 for secure, scalable data storage, reducing costs by 15%. Developed MongoDB schemas for structured and unstructured data, implementing replication and sharding to ensure high availability. Automated data transformation with Python, Spark SQL, and Pandas, enabling efficient data analysis and preparation.Utilized AWS Kinesis for real-time data streaming and AWS Lambda for cost-effective serverless workflows. Monitored and troubleshot applications using AWS CloudWatch, ensuring system reliability. Streamlined CI/CD processes with AWS CodePipeline and CodeBuild to ensure smooth deployments.Enhanced real-time analytics with interactive visualizations using Presto, D3.js, and Redshift Spectrum, boosting accuracy and engagement by 20%. Optimized queries with Apache Hive, reducing processing times by 35%. Improved collaboration in Databricks, increasing team productivity by 25%.Implemented Docker and Kubernetes for scalable and reliable containerized applications. Established secure data lakes with AWS Lake Formation, enforcing governance and compliance policies. Automated data cataloging for consistent metadata management.Enhanced Amazon Redshift performance by 25% through optimized queries and ensured seamless integration of SQL and NoSQL datasets. Leveraged NumPy and Pandas for advanced data analysis, delivering actionable insights. Ensured compliance with security policies and applied governance best practices to maintain data integrity and traceability.

Optimized query performance on Amazon S3 using partitioning and indexing, significantly reducing response times. Designed secure data storage solutions on Amazon S3 with AWS IAM policies to enhance access control and data security. Automated data transformation workflows using Python, AWS Lambda, and Boto3, reducing manual tasks by 40%.Established secure data lakes with AWS Lake Formation, improving governance compliance by 35%. Developed and optimized ETL processes using Informatica, Apache Spark, and AWS Lambda, achieving a 30% increase in efficiency. Built data models in Snowflake and Amazon Athena for high-performance querying and reporting.Created and managed data pipelines with AWS Step Functions and Boto3, automating workflows and complex processes. Leveraged Apache Spark, Hadoop, and Amazon EMR for distributed data processing. Integrated Kafka for real-time streaming with sub-second latency, enhancing data availability. Improved collaboration using Databricks’ shared workspace, increasing team productivity by 25%.Orchestrated workflows with Luigi and Kubernetes, reducing deployment time by 40%. Enhanced Snowflake performance through partitioning and indexing, reducing query times by 30%. Applied data governance and compliance practices, ensuring secure data handling and access through AWS IAM.Optimized Kubernetes pod scheduling and resource allocation to reduce costs and improve cluster performance. Delivered actionable insights via SQL and Matplotlib, supporting analytics and business intelligence. Ensured data integrity and security while managing efficient data movement across the lifecycle.

Designed and implemented scalable data pipelines to automate ETL processes across multiple cloud environments using Azure Synapse Analytics. Managed data integration between Azure SQL Database, ADLS, and other storage platforms, ensuring seamless and secure data movement. Leveraged Azure Functions for real-time data processing and integrated Azure Stream Analytics for continuous data stream analysis.Optimized data workflows using PySpark and Apache Spark for distributed data processing, and implemented validation frameworks to ensure data accuracy across ETL pipelines. Developed advanced SQL queries for data extraction and reporting, while utilizing Pandas and NumPy for large-scale data analysis in Python.Collaborated with stakeholders to create interactive dashboards using Power BI, delivering actionable insights. Automated administrative tasks with PowerShell and enhanced collaboration using Databricks’ shared workspace features. Developed and maintained CI/CD pipelines using Azure DevOps and streamlined version control with BitBucket.Integrated MongoDB into cloud workflows and designed schema models to optimize data storage and retrieval. Applied best practices in data security, governance, and compliance while facilitating efficient data transfer and storage through Azure Synapse Analytics and ADLS. Contributed to high-performance data workflows that supported real-time analytics and business intelligence initiatives.

Designed and optimized scalable data pipelines using GCP Cloud Storage and BigQuery to enable seamless data movement and efficient querying. Developed real-time data processing workflows with Dataflow, Dataproc, and Databricks Structured Streaming to handle high-velocity data for real-time analytics. Automated ETL workflows using Talend and leveraged Cloud Functions to enhance scalability and reliability.Managed containerized applications with Docker and Kubernetes, implementing CI/CD pipelines and fault-tolerant infrastructure for high availability. Integrated Apache Airflow for workflow orchestration and optimized data models in BigQuery for structured, scalable storage solutions. Monitored performance and conducted troubleshooting with Stackdriver, ensuring efficient operations. Collaborated with cross-functional teams to deliver data solutions aligned with business goals using Agile and Kanban methodologies.

India

Proficient in automating data workflows using Python and processing complex datasets with Pandas, NumPy, and SQL. Experienced in ETL development with Talend, creating interactive dashboards with Tableau and Power BI, and managing large-scale data pipelines with Apache Spark and Hadoop. Skilled in database management with Oracle, Snowflake, and MongoDB, and leveraging AWS and Azure services for scalable cloud solutions. Strong in collaboration using Agile methodologies, version control (Git), and task management tools (JIRA). Adept at delivering actionable insights through advanced data visualization and analytics to solve business challenges effectively.