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|PROFESSIONAL SUMMARY                                      |                             ||Over 7+ of hands-on expertise as a Senior Data Engineer   |TECHNICAL SKILLS:            ||specializing in Database Development, ETL Development,    |                             ||Data Modeling, Report Development, and Big Data           |Cloud Platforms: Amazon web  ||Technologies.                                             |Services (AWS), Microsoft    ||Proficient in programming languages like Python (Pandas,  |Azure, Google Cloud Platform ||NumPy, PySpark, scikit-learn, PyTorch), SQL (including    |(GCP)                        ||PL/SQL for Oracle), Scala, and PowerShell.                |Big Data Processing and      ||Extensive experience with cloud platforms including AWS   |Analytics: Apache Spark,     ||(S3, Redshift, RDS, DynamoDB, EMR, Glue, Data Pipeline,   |Apache Airflow, Hadoop, Hive,||Kinesis, Athena, QuickSight, Lambda, CloudFormation,      |Sqoop, Kafka, Impala, Apache ||CodePipeline), Azure (ADF, SQL Server, Cosmos DB,         |Beam                         ||Databricks, HDInsight, Blob Storage, Data Lake Storage),  |Programming and Scripting:   ||and Google Cloud Platform (BigQuery, Dataflow, Dataproc,  |Python (Pandas, NumPy,       ||Pub/Sub, Cloud Storage, Cloud SQL, Cloud Datastore, Cloud |PySpark, scikit-learn,       ||Pub/Sub, Apache Beam).                                    |PyTorch), Java Spring        ||Proficient in Apache Spark, Apache Airflow, Hadoop, Hive, |Framework, SQL (including    ||Sqoop, Kafka, Impala, and Apache Beam for large-scale data|PL/SQL for Oracle), Scala,   ||processing and analytics.                                 |PowerShell                   ||Experience with cloud data warehouses like Redshift,      |Data Integration and ETL     ||Snowflake, and BigQuery for scalable data storage and     |Tools: AWS Glue, AWS Data    ||retrieval.                                                |Pipeline, Informatica,       ||Skilled in data visualization tools such as Tableau, Power|Talend, SSIS                 ||BI, Google Data Studio, and QuickSight for creating       |Containerization and         ||insightful reports and dashboards.                        |Orchestration: Docker,       ||Hands-on experience with data integration tools like      |Kubernetes                   ||Informatica, Talend, and SSIS for seamless data flow      |                             ||across systems.                                           |Version Control and          ||In-depth knowledge of various database systems including  |Collaboration: Git, GitHub,  ||SQL Server, Cosmos DB, Oracle, PostgreSQL, Cassandra,     |Bitbucket                    ||MySQL, and DynamoDB for efficient data storage and        |CI/CD: Azure DevOps, Jenkins,||retrieval.                                                |AWS CodePipeline             ||Proficient in handling data formats like JSON, XML, and   |Data Warehousing and Database||Avro for data interchange and storage.                    |Management: Redshift,        ||Familiarity with containerization technologies like Docker|Snowflake, BigQuery, SQL     ||and orchestration tools like Kubernetes for scalable and  |Server, Cosmos DB, Oracle,   ||manageable deployments.                                   |PostgreSQL, Cassandra, MySQL,||Experience with CI/CD pipelines using Azure DevOps,       |DynamoDB                     ||Jenkins, and AWS CodePipeline for automated software      |Data Visualization and BI    ||delivery and deployment.                                  |Tools: Tableau, Power BI,    ||Proficient in Excel Advanced functions, pivot tables, and |Google Data Studio,          ||V Lookups for data analysis and reporting.                |QuickSight                   ||Hands-on experience with AWS Glue, AWS Data Pipeline for  |Security and Access Control: ||ETL workflows and data processing.                        |AWS IAM and AWS KMS, Azure   ||Familiarity with version control systems like Git, GitHub,|Key Vault and Azure AD,      ||and Bitbucket for collaborative development and code      |SSL/TLS, AES encryption      ||management.                                               |standards                    ||Strong understanding of security and access control       |Miscellaneous Tools and      ||principles including AWS IAM, AWS KMS, Azure Key Vault,   |Technologies: JSON, Excel    ||Azure AD, SSL/TLS, and AES encryption standards.          |Advanced functions, pivot    ||Proficient in project management tools like Bugzilla,     |tables, V Lookups, Bugzilla, ||Confluence, SharePoint, JIRA, Agile, Scrum, and Kanban for|Confluence, SharePoint, JIRA,||efficient project execution and collaboration.            |Agile, Scrum, Kanban         ||Analyzed business needs, designed efficient processes, and|Operating Systems: Window,   ||managed development teams to deliver successful projects. |Linux, UNIX, macOS           ||Implemented data pipelines, ensured system integration,   |                             ||and adapted to new technologies for continuous            |EDUCATION:                   ||improvement.                                              |Bachelors                    ||Collaborated with stakeholders at all levels to align     |CERTIFICATIONS:              ||project goals and ensure informed decision-making.        |Amazon Web Services(AWS)     ||Tackled complex data challenges with strong analytical    |JAVA SpringBoot              ||skills and a drive to contribute to a dynamic and         |Software Development Courses ||innovative environment.                                   |                             ||WORK EXPERIENCE:                                                                        ||                                                                                        ||J.P. Morgan Chase & Co., Plano, TX                                                      ||Sr. Data Engineer | Oct 2023 - Present                                                  ||Led the software development lifecycle (SDLC) for data engineering projects, from       ||requirements gathering to deployment and maintenance, ensuring quality and efficiency   ||throughout the process.                                                                 ||Managed data storage and retrieval using Amazon S3, optimizing data storage and access  ||patterns for scalability and performance.                                               ||Designed and implemented data warehouse solutions using Amazon Redshift, ensuring       ||efficient data modeling and query performance for analytics.                            ||Managed relational databases using Amazon RDS, ensuring data integrity, availability,   ||and performance.                                                                        ||Implemented NoSQL database solutions using Amazon DynamoDB, enabling scalable and       ||flexible data storage for various use cases.                                            ||Utilized Amazon EMR for big data processing and analytics, leveraging HDFS, MapReduce,  ||Hive, and Pig for distributed computing tasks.                                          ||Implemented ETL processes using AWS Glue and AWS Data Pipeline, ensuring seamless data  ||integration and transformation.                                                         ||Managed real-time data streams using Amazon Kinesis, enabling stream processing and     ||real-time analytics.                                                                    ||Utilized Amazon Athena for interactive query processing, enabling ad-hoc analysis of    ||data stored in S3.                                                                      ||Developed interactive dashboards and reports using Amazon QuickSight, providing business||intelligence insights to stakeholders.                                                  ||Leveraged serverless computing with AWS Lambda for event-driven data processing and     ||automation.                                                                             ||Utilized Apache Spark for distributed data processing and analytics, optimizing data    ||workflows and performance.                                                              ||Orchestrated data workflows using Apache Airflow, ensuring automation and scheduling of ||data pipelines.                                                                         ||Applied SQL, Python, and PySpark for data manipulation, analysis, and machine learning  ||model development, enhancing data processing capabilities.                              ||Utilized Scala for Spark programming, optimizing Spark code for performance and         ||scalability.                                                                            ||Managed and analyzed data using Pandas and NumPy, ensuring efficient data processing and||analysis workflows.                                                                     ||Implemented columnar storage using Apache Parquet, optimizing data storage and query    ||performance.                                                                            ||Processed and transformed XML data formats, enabling structured data processing and     ||integration.                                                                            ||Utilized ERwin for data modeling and database design, ensuring data integrity and       ||consistency.                                                                            ||Managed access control and encryption using AWS IAM and AWS KMS, ensuring data security ||and compliance.                                                                         ||Implemented encryption standards including SSL/TLS and AES, ensuring data protection    ||during transmission and storage.                                                        ||Implemented data anonymization techniques and data governance policies, ensuring data   ||privacy and compliance with regulations.                                                ||Monitored and managed AWS resources using AWS CloudWatch and AWS CloudTrail, ensuring   ||performance optimization and security.                                                  ||Managed code repositories and collaborated with teams using Git, ensuring version       ||control and code quality.                                                               ||Managed project workflows and tasks using JIRA, ensuring collaboration and alignment    ||with project goals and timelines.                                                       ||Automated infrastructure deployment using AWS CloudFormation, ensuring consistent and   ||scalable infrastructure configurations.                                                 ||Implemented continuous integration and continuous deployment (CI/CD) pipelines using AWS||CodePipeline, ensuring automated and reliable software delivery.                        ||Containerized applications using Docker, enabling scalable and portable deployment of   ||data solutions.                                                                         ||Orchestrated containerized applications using Kubernetes, ensuring efficient management ||and scaling of containerized workloads.                                                 ||Contributed to Agile methodologies, participating in Scrum ceremonies and sprint        ||planning to deliver data solutions iteratively and efficiently.                         ||Tech Stack: AWS, Redshift, DynamoDB, EMR, AWS Glue, Kinesis, Athena, QuickSight, AWS    ||Lambda, HDFS, MapReduce, Hive, Pig, Spark, Airflow, SQL, Python, PySpark, Scala,        ||Parquet, XML, ERwin, IAM, KMS, CloudWatch, CloudTrail, GIT, JIRA , AWS CloudFormation,  ||Docker, Kubernetes, Agile (Scrum), JIRA.eer                                             ||                                                                                        ||                                                                                        ||                                                                                        ||                                                                                        ||American Airlines, DFW, TX                                                              ||Data Engineer | Aug 2022 - Sep 2023                                                     ||Designed and implemented data integration workflows using Azure Data Factory (ADF),     ||ensuring seamless data movement and transformation across on-premises and cloud         ||environments.                                                                           ||Managed and optimized SQL Server databases, ensuring data integrity, performance, and   ||availability for business operations.                                                   ||Implemented Azure Cosmos DB for globally distributed and scalable NoSQL database        ||solutions, ensuring high availability and low-latency data access.                      ||Utilized Snowflake for cloud-based data warehousing, enabling scalable and flexible     ||analytics solutions.                                                                    ||Implemented data processing and analytics workflows using Azure Databricks, leveraging  ||Apache Spark for distributed computing and machine learning.                            ||Managed and optimized big data clusters using Azure HDInsight, ensuring efficient data  ||processing and analytics capabilities.                                                  ||Utilized Azure Blob Storage and Azure Data Lake Storage for scalable and cost-effective ||data storage solutions.                                                                 ||Automated tasks and workflows using PowerShell, streamlining data management and        ||operations.                                                                             ||Applied Python with Pandas, NumPy, and PyTorch for data manipulation, analysis, and     ||machine learning model development, enhancing data processing capabilities.             ||Implemented data processing pipelines using Spark, handling large-scale data processing ||and analytics tasks.                                                                    ||Developed serverless functions using Azure Functions, enabling event-driven data        ||processing and automation.                                                              ||Managed and optimized Hadoop clusters for distributed data processing and analytics,    ||ensuring scalability and performance.                                                   ||Implemented Kafka for real-time data streaming and processing, enabling real-time       ||analytics and event-driven architectures.                                               ||Managed secrets and access control using Azure Key Vault and Azure Active Directory     ||(Azure AD), ensuring data security and compliance.                                      ||Processed and analyzed JSON data formats, enabling structured data processing and       ||integration.                                                                            ||Managed code repositories and collaborated with teams using Bitbucket, ensuring version ||control and code quality.                                                               ||Utilized Impala for interactive SQL queries and analytics on Hadoop-based data          ||platforms.                                                                              ||Implemented continuous integration and continuous deployment (CI/CD) pipelines using    ||Azure DevOps, ensuring automated and reliable software delivery.                        ||Monitored and managed Azure resources using Azure Monitor and Azure Log Analytics,      ||ensuring performance optimization and troubleshooting.                                  ||Automated infrastructure deployment and management using Terraform, ensuring consistent ||and scalable infrastructure configurations.                                             ||Containerized applications and services using Docker, enabling scalable and portable    ||deployment of data solutions.                                                           ||Orchestrated containerized applications using Kubernetes, ensuring efficient management ||and scaling of containerized workloads.                                                 ||Developed and deployed interactive data visualizations using Power BI, enabling         ||data-driven insights and decision-making.                                               ||Contributed to Agile methodologies, participating in Scrum ceremonies and sprint        ||planning to deliver data solutions iteratively and efficiently.                         ||Managed project workflows and tasks using JIRA, ensuring collaboration and alignment    ||with project goals and timelines.                                                       ||Tech Stack: ADF, SQL Server, Azure Cosmos DB, Snowflake, Azure Databricks, Azure        ||HDInsight, Azure Blob Storage, Azure Data Lake Storage, PowerShell, Python, Spark, Azure||Functions, Hadoop, Kafka, JSON, Bitbucket, Impala, Azure DevOps, Azure Monitor,         ||Terraform, Docker, Kubernetes, Power BI, JIRA.                                          ||                                                                                        ||SONY Play Station , San Mateo, CA                                                       ||Data Engineer | Oct 2021 - Aug 2022                                                     ||Implemented Pub/Sub and Cloud Storage for real-time data ingestion and storage, ensuring||reliable and scalable data pipelines.                                                   ||Managed Cloud SQL and Cloud Datastore for structured and unstructured data storage,     ||maintaining data integrity and accessibility.                                           ||Designed and implemented data streaming pipelines using Cloud Pub/Sub, Apache Beam, and ||Apache Kafka, enabling real-time data processing and analytics.                         ||Leveraged Apache Spark and Hadoop for distributed data processing and analytics,        ||handling large-scale datasets efficiently.                                              ||Orchestrated data workflows using Apache Airflow, ensuring automation and scheduling of ||data pipelines for timely processing.                                                   ||Integrated data sources using Sqoop and Informatica, facilitating seamless data         ||extraction, transformation, and loading processes.                                      ||Utilized Python with Pandas and NumPy for data manipulation, analysis, and modeling,    ||enhancing data processing capabilities.                                                 ||Developed data visualizations and dashboards using Data Studio and Google Analytics,    ||providing actionable insights to stakeholders.                                          ||Managed code repositories and collaborated with teams using GitHub, ensuring version    ||control and code quality.                                                               ||Orchestrated containerized applications using Docker and Kubernetes, ensuring           ||scalability and reliability of deployed data solutions.                                 ||Automated infrastructure deployment and management using Terraform, optimizing resource ||utilization and cost efficiency.                                                        ||Implemented continuous integration and deployment pipelines using Jenkins, ensuring     ||seamless delivery of data solutions.                                                    ||Utilized ELK Stack (Elasticsearch, Logstash, Kibana) for log analysis and monitoring,   ||ensuring data visibility and troubleshooting capabilities.                              ||Handled data serialization using Avro, ensuring efficient data storage and processing.  ||Managed relational databases including PostgreSQL and NoSQL databases like Cassandra,   ||ensuring data availability and performance.                                             ||Developed and deployed machine learning models using TensorFlow, enhancing data         ||analytics and predictive capabilities.                                                  ||Utilized VS Code for code development and debugging, ensuring code efficiency and       ||reliability.                                                                            ||Contributed to Agile and Kanban methodologies, participating in sprint planning, daily  ||stand-ups, and backlog grooming to deliver data solutions efficiently.                  ||Collaborated and documented technical specifications using Confluence, ensuring         ||knowledge sharing and documentation of data solutions.                                  ||Tech Stack: Apache Beam, Apache Spark, Apache, Airflow, Hadoop, Kafka, Sqoop,           ||Informatica, Python, Scala, Data Studio, Google Analytics, GitHub, Terraform, Jenkins,  ||ELK Stack, Avro, PostgreSQL, Cassandra, TensorFlow,  VS Code, Agile, Kanban, Confluence.||                                                                                        ||                                                                                        ||Dun&Bradstreet (Cognizant), India                                                       ||Data Engineer | Feb 2019 - Sep 2021                                                     ||Utilized Hadoop, Spark, and Hive to process and analyze large volumes of data,          ||optimizing performance and scalability for big data applications.                       ||Implemented Sqoop for efficient data transfer between Hadoop and relational databases,  ||ensuring seamless data integration and synchronization.                                 ||Developed complex SQL and PL/SQL queries to extract, transform, and load data from      ||diverse sources into data warehouses, improving data accessibility and analysis         ||capabilities.                                                                           ||Managed AWS resources including EC2 instances, S3 buckets, RDS databases, and Lambda    ||functions, leveraging cloud services for scalable and cost-effective data solutions.    ||Utilized Python with NumPy and Pandas for data manipulation, statistical analysis, and  ||machine learning model development, enhancing data processing workflows.                ||Maintained version control and collaborated with teams using Git, ensuring code quality,||and facilitating efficient project management.                                          ||Designed and implemented ETL processes using SSIS (SQL Server Integration Services),    ||ensuring data quality and consistency across data pipelines.                            ||Managed bug tracking and issue resolution using Bugzilla, ensuring data integrity and   ||timely resolution of data-related issues.                                               ||Contributed to Agile and Kanban methodologies, participating in sprint planning, daily  ||stand-ups, and backlog grooming to deliver data solutions efficiently.                  ||Developed interactive dashboards and visualizations using Tableau, providing            ||stakeholders with actionable insights and data-driven decision-making capabilities.     ||Collaborated with SharePoint for document management and collaboration, ensuring data   ||governance and compliance with organizational standards.                                ||Implemented data security measures and access controls, ensuring data privacy and       ||compliance with regulatory requirements.                                                ||Conducted performance tuning and optimization of database queries and processes,        ||improving data processing efficiency and reducing latency.                              ||Participated in data architecture design and data modeling activities, ensuring         ||scalability, flexibility, and performance of data solutions.                            ||Provided technical expertise and support to cross-functional teams, contributing to the ||successful delivery of data projects and initiatives.                                   ||Tech Stack: Hadoop, Spark, Hive, Sqoop, SQL, PL/SQL, AWS, EC2, S3, RDS, Lambda, Python  ||(NumPy, Pandas), Git, SSIS, Bugzilla, Agile, Kanban, Tableau.                           ||                                                                                        ||MINFY Technologies, India                                                               ||Data Analyst/ Engineer | June 2017 - Jan 2019                                           ||Utilized Python, pandas, NumPy, and scikit-learn for data cleaning, preprocessing,      ||analysis, and machine learning model development, resulting in improved data quality and||predictive accuracy.                                                                    ||Leveraged SQL to query, manipulate, and extract insights from large datasets stored in  ||Oracle databases, ensuring data integrity and optimizing data retrieval performance.    ||Demonstrated expertise in Excel Advanced functions, pivot tables, and V Lookups to      ||create interactive dashboards and reports for stakeholders, facilitating data-driven    ||decision-making processes.                                                              ||Implemented Apache Spark for big data processing and analytics, handling large-scale    ||datasets efficiently and performing distributed computing tasks for faster data         ||processing.                                                                             ||Designed and implemented data integration workflows using Talend, ensuring seamless data||flow between heterogeneous systems and maintaining data consistency across platforms.   ||Managed version control and collaborated with teams using Git, tracking changes and     ||resolving issues efficiently to maintain code quality and project progress.             ||Utilized Bugzilla for bug tracking and issue management, ensuring timely resolution of  ||data-related issues and maintaining data accuracy.                                      ||Applied data engineering techniques in Hadoop ecosystem, including HDFS, Hive, and      ||HBase, to store, process, and analyze large volumes of structured and unstructured data.||                                                                                        ||Collaborated with cross-functional teams to develop and deploy data pipelines and ETL   ||processes, ensuring data availability and reliability for business analytics and        ||reporting.                                                                              ||Contributed to data governance initiatives by establishing data quality standards,      ||monitoring data quality metrics, and implementing data cleansing and enrichment         ||strategies.                                                                             ||Tech Stack: Python, pandas, NumPy, scikit-learn, SQL, Excel Advanced functions, pivot   ||tables, V Lookups, Spark, Talend, Oracle, Hadoop, Hive, HBase, Git, Bugzilla.           |-----------------------      Candidate's Name
      Sr. Data Engineer      EMAIL AVAILABLE      PHONE NUMBER AVAILABLE

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