pavan User

Architected scalable data pipelines using Hadoop and Spark, processing terabytes of telecom data daily. Conducted performance tuning of Hadoop and Spark jobs, developed advanced analytics models, and automated data workflows using Apache Airflow and Google Cloud services.

Architected scalable data pipelines using Hadoop, Spark, and Pyspark, processing terabytes of telecom data daily. Conducted performance tuning of Hadoop and Spark jobs, developed advanced analytics models, and managed complex ETL workflows using various tools and technologies.

Optimized Hadoop and Spark jobs for efficient data processing and developed robust ETL processes with Informatica. Leveraged GCP services for scalable data analytics and built interactive dashboards using Tableau and Power BI.

Worked on Hadoop ecosystem and big data technologies, developing ETL processes with PySpark and managing NoSQL databases. Implemented data warehousing solutions and collaborated with data scientists for optimized reporting.

Optimized Hadoop and Spark jobs for efficient data processing, developed ETL processes with Informatica, and utilized Google Cloud services for scalable data analytics. Created automated data workflows and dashboards using Tableau and Power BI.

Worked on Hadoop ecosystem and big data technologies, developed ETL processes with PySpark, and managed NoSQL databases. Implemented data sharing solutions in Snowflake and collaborated with data scientists for reporting needs.

Developed and maintained a Data Lake using Hadoop technologies, created ETL jobs for data extraction and transformation, and implemented data migration processes using AWS and Jenkins.

Developed and maintained a Data Lake using Hadoop technologies, created ETL jobs for data migration, and monitored production jobs. Engaged in data integration programs and collaborated with business teams to meet project requirements.

Architected scalable data pipelines using Hadoop, Spark, and Pyspark, efficiently processing terabytes of telecom data daily, resulting in enhanced data processing capabilities. Developed advanced analytics and machine learning models using PySpark, enabling real-time data processing and predictive insights in telecom operations.

Architected scalable data pipelines using Hadoop, Spark, and Pyspark, efficiently processing terabytes of telecom data daily, resulting in enhanced data processing capabilities. Developed advanced analytics and machine learning models using PySpark, enabling real-time data processing and predictive insights in telecom operations.

Conducted performance tuning and optimization of Hadoop and Spark jobs, ensuring efficient data processing and reducing job runtimes. Developed robust ETL processes with Informatica, extracting, transforming, and loading data from various sources into Snowflake and other data warehouses.

Worked on latest developments in Hadoop ecosystem and big data technologies, exploring emerging trends such as Apache Spark, Apache Flink, and Apache Kafka to continuously enhance data processing capabilities. Developed and managed ETL processes with PySpark, ensuring efficient data extraction, transformation, and loading across various data sources and sinks.

Conducted performance tuning and optimization of Hadoop and Spark jobs, ensuring efficient data processing and reducing job runtimes. Developed robust ETL processes with Informatica, extracting, transforming, and loading data from various sources into Snowflake and other data warehouses.

Worked on latest developments in Hadoop ecosystem and big data technologies, exploring emerging trends such as Apache Spark, Apache Flink, and Apache Kafka to continuously enhance data processing capabilities. Developed and managed ETL processes with PySpark, ensuring efficient data extraction, transformation, and loading across various data sources and sinks.

Developing and maintaining a Data Lake containing regulatory data for federal reporting with big data technologies such as Hadoop Distributed File System (HDFS), Apache Impala, Apache Hive and Cloudera distribution. Developing different ETL jobs to extract data from different data sources like Oracle, Microsoft SQL Server, transform the extracted data using Hive Query Language (HQL) and load it in

Developing and maintaining a Data Lake containing regulatory data for federal reporting with big data technologies such as Hadoop Distributed File System (HDFS), Apache Impala, Apache Hive and Cloudera distribution. Involved in importing the data from different sources into HDFS using sqoop and applying transformations using Hive.