Designed and executed cloud-native solutions on AWS with a focus on cost-effectiveness and performance optimization, utilizing services like Amazon S3, AWS Lambda, and Amazon EKS.Oversaw the transition of workloads from on-premises to AWS cloud-native architecture, which enhanced the applications' scalability, dependability, and security.AWS Code Pipeline and AWS Code Build were used to develop CI/CD pipelines that automate build, test, and deployment procedures, lowering manual… Show more Designed and executed cloud-native solutions on AWS with a focus on cost-effectiveness and performance optimization, utilizing services like Amazon S3, AWS Lambda, and Amazon EKS.Oversaw the transition of workloads from on-premises to AWS cloud-native architecture, which enhanced the applications' scalability, dependability, and security.AWS Code Pipeline and AWS Code Build were used to develop CI/CD pipelines that automate build, test, and deployment procedures, lowering manual errors and deployment times.Server-less architectures were implemented on AWS with enhanced resource utilization and significant cost reductions through the use of API Gateway and AWS Lambda functions.Created Python scripts to automate the ETL process with UNIX operating system CRON scripts and Apache Airflow scripts.Developed an ETL pipeline to ingest data from various sources utilizing Spark and Hive. Show less

Developed data ingestion jobs into various data storage systems in Azure and other enterprise data stores utilizing various streaming services, such as Apache Kafka.Constructed scalable data pipelines using Azure Data Bricks and assimilated enrichment on the data lake's gold layer.Designed and implemented Python-based data pipeline use cases for customer operator jobs in Azure Integration Services.Learn about use Azure Data Factory to migrate data from an on-premises… Show more Developed data ingestion jobs into various data storage systems in Azure and other enterprise data stores utilizing various streaming services, such as Apache Kafka.Constructed scalable data pipelines using Azure Data Bricks and assimilated enrichment on the data lake's gold layer.Designed and implemented Python-based data pipeline use cases for customer operator jobs in Azure Integration Services.Learn about use Azure Data Factory to migrate data from an on-premises environment to an Azure one.Using Azure Integration Services, an experienced ETL resource may automate data processing pipelines and monitor operations from start to finish.I worked on moving ETL workloads and datasets from on-premises to Azure Cloud.created a Python script to download files, then used the script to process ETL data using SQL stored procedures in an SSIS package.Developed scalable and reliable data integration pipelines utilizing Python and data integration tools to move data to a Redshift database.Developed scripts with PySpark to automate the ingestion process for a variety of sources, including Redshift, Teradata, and APIs.Utilized Spark SQL and Data Frames API extensively when developing apps with Spark.Developed a data pipeline that applies the transformation and ingests data from their weblog server using Flume, Kafka, and Spark Stream.Developed data ingestion jobs into various data storage systems in Azure and other enterprise data stores utilizing various streaming services, such as Apache Kafka.Constructed scalable data pipelines using Azure Data Bricks and assimilated enrichment on the data lake's gold layer.Designed and implemented Python-based data pipeline use cases for customer operator jobs in Azure Integration Services.Learn about use Azure Data Factory to migrate data from an on-premises environment to an Azure one. Show less

To simplify data storage and analysis, a centralized data repository was designed and put into place utilizing relational databases, data lakes, and SQL and NoSQL databases.Using Jupyter Notebook and Google Analytics, I implemented and tracked data analytics solutions that gave me insights into user behavior and business performance.Utilizing SQL, NoSQL, and R, I completed data integration and migration operations across many platforms to guarantee smooth data flow and… Show more To simplify data storage and analysis, a centralized data repository was designed and put into place utilizing relational databases, data lakes, and SQL and NoSQL databases.Using Jupyter Notebook and Google Analytics, I implemented and tracked data analytics solutions that gave me insights into user behavior and business performance.Utilizing SQL, NoSQL, and R, I completed data integration and migration operations across many platforms to guarantee smooth data flow and consistency.Utilizing IBM SPSS and SAS, I carried out advanced analytics and reporting, offering thorough data analysis to assist in business intelligence and decision-making.Using Tableau, I created analytical dashboards and interactive visualizations by extracting and presenting data using R and SQL.Improved performance and scalability of data operations through the optimization of SQL queries and NoSQL database settings, leading to an increase in data processing efficiency.R and Python were integrated to provide thorough data exploration, while Jupyter Notebook was used for exploratory data analysis and reproducible research creation.Worked together with cross-functional teams to specify data needs and created intricate ETL pipelines with SAS and SQL to load and transform data into centralized systems from a variety of sources while guaranteeing high data availability and correctness.ER diagrams and Rational Rose were used to create and manage dynamic data models, which successfully mapped relationships between different data entities for better database design.Utilizing R and Python in a Jupyter Notebook, one could conduct sophisticated statistical analysis and predictive modeling, yielding valuable insights that influenced business choices.Enhanced data extraction and transformation procedures with the application of NoSQL and SQL technologies, guaranteeing effective data processing and cross-platform integration. Show less

Improved end-to-end construction of data marts and warehouses, leveraging NoSQL and SQL technologies to streamline ETL procedures and combine information from many platforms.Utilized sophisticated techniques like partitioning, clustering, and indexing to improve the efficiency of SQL query performance tuning and optimization.To expedite data processing and boost operational effectiveness, automated data workflows and templates were created and integrated with data storage… Show more Improved end-to-end construction of data marts and warehouses, leveraging NoSQL and SQL technologies to streamline ETL procedures and combine information from many platforms.Utilized sophisticated techniques like partitioning, clustering, and indexing to improve the efficiency of SQL query performance tuning and optimization.To expedite data processing and boost operational effectiveness, automated data workflows and templates were created and integrated with data storage systems.Developed and maintained ETL process architecture, utilizing NoSQL and SQL databases in conjunction with contemporary data technologies to guarantee efficient data handling and transformation.Designed and automated data pipelines with cutting-edge tools, combining data from various sources into coherent models for thorough reporting and analysis.Oversaw substantial amounts of historical data kept in cloud storage, guaranteeing efficient data handling.Using tools like IBM SPSS, Jupyter Notebook, and Azure, I oversaw the end-to-end project deployment, including data analysis, data pipelining, modeling, and reporting, all in alignment with business goals and objectives.Performed performance evaluation and enhancement of data processing tasks in Azure, cutting down on execution durations and resource expenses by optimizing performance and utilizing Azure SQL Database and Azure.Utilizing Rational Rose and ER diagrams, we developed and improved data models and schemas utilizing Azure to produce accurate and pertinent reporting and analytics.Developed and oversaw dashboards and reports using Google Analytics and data visualization tools to support strategic decision-making and offer actionable insights.Automated data transformation procedures that guarantee precise and fast data availability for analysis, utilizing tools like SAS for sophisticated data manipulation.Workflows and pipelines for data optimization. Show less