Bharath Kumar Email & Phone Number

New York, New York, Us

• Designed and managed scalable storage solutions using GCP Cloud Storage, optimizing data organization, retrieval, and lifecycle management for cost-effective handling of large-scale structured and unstructured data.• Optimized complex queries on BigQuery for large-scale datasets, reducing processing time and enhancing performance.• Integrated machine learning models using Vertex-AI for automated predictions and insights.• Developed scalable data lakes using GCP Cloud Storage for managing both structured and unstructured data.• Orchestrated data workflows with Apache Airflow on Google Composer to automate and streamline ETL processes.• Designed real-time data streaming pipelines using Pub/Sub to facilitate seamless data ingestion and integration.• Built and optimized data pipelines using GCP services such as BigQuery, Cloud Storage, and Composer, improving data processing efficiency.• Integrated predictive models into data workflows using Vertex-AI, enhancing real-time analytics and forecasting accuracy.• Developed real-time data ingestion pipelines with Pub/Sub, enabling seamless integration of event data into analytics systems.reducing operational overhead by 35%.• Optimized data processing workflows using PySpark, enhancing the speed and efficiency of handling large-scale datasets and improving overall pipeline performance.• Built robust ETL pipelines with PySpark, enabling seamless data transformations and ensuring data consistency across distributed systems.• Developed scalable data applications in PySpark to process high-volume data in real-time, achieving faster processing times and greater data accuracy.• Developed data processing applications in Scala for high-performance data transformations, enabling faster, more efficient handling of large datasets.• Implemented complex algorithms using Scala to streamline data processing workflows, improving computational efficiency and reducing execution times.

New York, Ny, Us

• Developed and maintained distributed data processing systems using Apache Hadoop, Spark, and Kafka, supporting real-time and batch analytics for 1 billion+ daily records. • Migrated on-premises data infrastructure to Google Cloud Platform (GCP), leveraging BigQuery for enhanced performance and cost savings.• Built ETL workflows using Talend, Airflow, and later integrated tools like Stitch and Fivetran to automate data ingestion. • Integrated Snowflake into the data pipeline, improving query response times by 30% and enhancing scalability for future data growth. • Optimized Google BigQuery to handle large-scale datasets, improving query efficiency and reducing processing times by 25%. • Implemented Delta Lake for efficient handling of ACID transactions in the data lake, improving data reliability and analytics workflows.insights and supporting business analytics.• Implemented PySpark streaming applications to process real-time data feeds, ensuring timely and accurate data availability for analytics and reporting.• Developed machine learning data preparation scripts in Scala, preparing data for model training and tuning processes, which enhanced model accuracy and performance.• Leveraged Scala’s strong typing and functional programming capabilities to create scalable, maintainable codebases, reducing technical debt and enhancing code readability.• Integrated Scala with cloud data platforms to enable seamless data ingestion and processing, supporting data pipelines that delivered insights in near real-time

San Francisco, California, Us

• Engineered high-performance data processing solutions in Scala, leveraging functional programming paradigms to enhance scalability and streamline data transformation tasks.• Utilized Scala’s concurrency features to build asynchronous, non-blocking data workflows, ensuring efficient processing of large volumes of data in real-time environments.• Created reusable data manipulation libraries in Scala, enabling consistent data transformation logic across multiple projects and improving development efficiency.• Built complex data pipelines in PySpark to handle data cleansing, transformation, and aggregation, achieving significant reductions in processing times for large datasets.• Optimized PySpark jobs through tuning and configuration, using advanced techniques to minimize execution time and resource consumption in distributed environments.• Implemented real-time streaming applications in PySpark, allowing for continuous data ingestion and processing, which supported dynamic analytics and reporting needs.• Assisted in developing Hadoop-based data lakes and ETL pipelines for processing large datasets, supporting internal analytics functions. • Built automated data validation tools using Python and SQL scripts, ensuring data accuracy and reducing manual data cleanup by 30%. • Supported Hadoop ecosystem management, optimizing Hive and Pig queries to improve processing times by 20%.• Assisted with migrating on-premises relational databases to AWS using Sqoop, enabling seamless transitions with no data loss.• Developed batch ETL workflows using Oozie and Sqoop, automating data ingestion into HDFS from various relational databases.• Collaborated with senior engineers to design and implement data pipelines for real-time analytics using Apache Kafka.

Bangalore, Karnataka, In

• Built and managed ETL pipelines using SSIS to extract data from Oracle and MySQL, transform it with custom scripts, and load it into a SQL Server data warehouse, supporting robust downstream analytics.• Enhanced database performance through careful query optimization, creating indexed views, partitioning large tables, and analyzing execution plans in both Oracle and MySQL environments.• Developed and maintained stored procedures and triggers in PL/SQL and T-SQL, handling complex data transformations and automating data workflows for batch processing.• Created and managed dimensional data models with star and snowflake schemas, aligning with business needs to support efficient reporting in a data warehouse setting.• Engineered data pipelines for structured and unstructured data using Python and shell scripts, ensuring proper data transformation and loading into both staging and production environments.• Designed and maintained data marts within SQL Server, aggregating data and creating optimized views to enhance reporting and analytics capabilities.• Automated batch data processing tasks by leveraging SQL Server Agent and cron jobs, integrating error-handling mechanisms to maintain reliability in large-scale ETL workflows.• Integrated data from multiple sources through scripting in Python and shell, streamlining data ingestion from relational databases and flat files into SQL-based systems for analysis and reporting.

Master Of Science - Ms, Computer Science

Bachelor Of Technology - Btech, Electrical And Electronics Engineering