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Click here or scroll down to respond to this candidate Rakesh BEMAIL AVAILABLE
LINKEDIN LINK AVAILABLEPHONE NUMBER AVAILABLEProfessional Summary: Over 5 years of professional experience as a Data Engineer, specializing in data analysis, design, coding, and the development of Data Warehousing solutions. Expert in managing data analytics, data processing, and data-driven projects, with strong capabilities in Machine Learning and Artificial Intelligence. Proficient in scripting with Python and SQL, and UNIX/Linux shell scripting, leveraging these skills to perform efficient Extract, Load, and Transform ETL operations. Experienced in real-time data processing using Apache Kafka and Apache Spark, constructing robust data pipelines for high-volume transactions. Skilled in developing and managing data warehousing solutions using Amazon Redshift, Snowflake, and MySQL, ensuring optimized data aggregation and analysis. Proficient with tools in the Hadoop Ecosystem including Hive, HDFS, MapReduce, Sqoop, Kafka, Yarn, and HBase, with expertise in handling terabytes of streaming and batch data. Experienced in utilizing AWS and Azure services like Azure Data Lake, Azure Data Factory, Databricks, Azure Synapse Analytics, Azure Stream Analytics, and Azure Event Hub for scalable and efficient data architectures. Strong background in data governance and security protocols using Collibra and IAM, ensuring compliance and safeguarding sensitive information. Adept at implementing CI/CD practices using Docker, Kubernetes, Terraform and Jenkins, streamlining deployment processes and improving development efficiency. Skilled in data visualization and reporting using Power BI and Tableau, creating interactive dashboards to support business decision-making. Extensive experience in database management and development using PostgreSQL, SQL Server, and MongoDB, optimizing data storage and retrieval performance. Experienced in Agile methodologies, including Scrum and SDLC best practices, ensuring high-quality and timely delivery of projects. Demonstrated ability to lead collaborative efforts with cross-functional teams, data analysts, and business stakeholders to deliver tailored data solutions.Technical Skills:Big Data ToolsMapReduce, HDFS, Yarn, Hive, Apache Spark, Apache Kafka, SqoopCloud Platforms and ServicesAzure: Azure Data Lake (ADLS), Azure Data Factory (ADF), Databricks, Azure Synapse Analytics, Azure Stream Analytics, Azure DevOps, Azure Event Hub AWS: EC2, S3, Glue, Lambda, CloudWatch, DynamoDB, SQSOthers: AWS Data Pipeline, EMR, Amazon Athena, Google Cloud PlatformNoSQL DatabasesMongoDB, Hadoop HBase and Apache CassandraProgramming & ScriptingPython, SQL, Scala, R, Java, PySpark, HiveQL, UNIX/Linux shell scripting, VBA,RESTful APIsData Warehousing and DatabaseManagementAmazon Redshift, Google BigQuery, Snowflake, MySQL, PostgreSQL, SQL Server,Oracle, DB2ETL and Data PipelinesApache Airflow, Apache NiFi, DBT, Informatica, SQL Server Integration Services(SSIS), TalendData Visualization and ReportingTableau, Power BI, Looker, SSRSVersion Control SystemsGit, BitbucketData Security and GovernanceIAM, Apache Atlas, CollibraDevOps ToolsDocker, Kubernetes, CI/CD, Terraform, Prometheus, Grafana, Jenkins, GitLab CIDevelopment ToolsEclipse, Visual Studio, SQL Server Management Studio (SSMS), NetBeans, IntelliJ,Microsoft Office Suite (Word, Excel, PowerPoint, Access)Professional Summary:Client: 7-Eleven Irving, Texas June 2023 PresentRole: Data EngineerResponsibilities: Architected and maintained ETL/ELT workflows utilizing Python and Apache Spark with Apache Airflow, enhancing data automation efficiency. Executed data cleansing, validation, and transformation on intricate financial datasets, ensuring superior data quality and consistency for downstream analytics using Snowflake. Led collaborative efforts with data analysts and business stakeholders to comprehend data requirements, resulting in tailored solutions meeting precise business needs, leveraging Azure and Agile methodologies. Constructed data pipelines for real-time processing using Apache Kafka & RESTful APIs, significantly improving performance and throughput. Administered and optimized NoSQL databases using Apache Cassandra, enhancing data storage and retrieval performance for high-volume transactions. Established robust data governance protocols to fortify data security and achieve regulatory compliance mandates, leveraging Collibra and Apache Spark. Implemented and managed data warehousing solutions using Amazon Redshift, reducing query times. Enhanced data models using Star schema design principles for efficient querying, improving query performance, and facilitating agile analytics with Azure Synapse Analytics. Engineered data processing frameworks using Databricks, improving data processing efficiency by 40%. Utilized Git for version control and IntelliJ for development, ensuring efficient collaboration and development processes. Deployed CI/CD pipelines using Docker, Kubernetes and Terraform for streamlined deployment and management. Created interactive dashboards and performed real-time data analytics using Spark and Power BI, improving decision-making processes and reducing reporting times by 50%. Leveraged Azure Stream Analytics to process and analyze streaming data in real-time, enhancing data processing capabilities and providing instant insights. Integrated Azure Event Hub for event-driven data processing, improving real-time data handling and reducing latency. Implemented and automated routine tasks using UNIX/Linux shell scripting to enhance operational efficiency.Client: Cisco - San Jose, California Mar 2021 Jul 2022 Role: Data EngineerResponsibilities: Conceptualized and executed scalable data pipelines to ingest, transform, and load large datasets, ensuring seamless data flow and integrity using MapReduce, HDFS, and Yarn. Utilized Python and SQL for programming tasks, enhancing data processing efficiency and performance. Implemented Big Data frameworks including Apache Hadoop to handle massive volumes of data efficiently, optimizing data analysis and machine learning initiatives. Leveraged AWS (EC2, S3, Lambda, CloudWatch) to ensure data quality, availability, and efficiency for diverse data analysis projects. Designed and maintained data warehousing solutions utilizing MySQL, facilitating streamlined data aggregation and analysis. Employed Talend for data integration and orchestration, automating workflows and enhancing operational efficiency. Created and maintained data visualization reports using Looker and SSRS, enhancing the accuracy and accessibility of business insights, supporting decision-making processes. Administered NoSQL databases using MongoDB, improving data storage flexibility and retrieval times, contributing to more efficient data handling. Applied IAM protocols to ensure data security and compliance with industry standards, safeguarding sensitive information. Adopted CI/CD practices to streamline deployment processes and improve development efficiency, reducing deployment times by 20%. Utilized Microsoft Office Suite (Word, Excel) and Jira for comprehensive documentation and reporting, enhancing communication and project tracking.Client: UnitedHealth Group - Minnetonka, Minnesota Aug 2018 Feb 2021 Role: Data EngineerResponsibilities: Developed and sustained data pipelines for member enrollment, claims processing, and risk management systems, employing Hive and Sqoop and RESTful APIs for seamless data integration and querying. Formulated data models using PostgreSQL and SQL Server, enhancing data storage and retrieval performance. Enforced stringent data quality checks and monitoring procedures, ensuring data accuracy and consistency within the data pipelines, leveraging Scala. Orchestrated data workflows using Informatica and SQL Server Integration Services (SSIS), enhancing automation and scheduling capabilities for improved operational efficiency. Collaborated closely with data scientists to prepare and transform data for machine learning models deployed in fraud detection and healthcare cost prediction initiatives, following SDLC best practices. Assisted in the migration of on-premise data infrastructure to cloud platforms (Azure Data Lake (ADLS) and Azure Data Factory (ADF)), ensuring scalability and cost-effectiveness, with a focus on HIPAA compliance practices. Leveraged Tableau for data visualization and reporting, creating interactive dashboards to support business decision- making. Utilized Bitbucket for version control, facilitating efficient collaboration and project management within the team. Managed databases using SQL Server Management Studio (SSMS), ensuring efficient data handling and processing. Employed Apache HBase for NoSQL database management, improving data storage flexibility and retrieval times. Integrated Grafana and Jenkins for monitoring and continuous integration, enhancing system reliability and deployment efficiency. Implemented data governance protocols using Apache Atlas, ensuring data compliance and security.EducationMaster of Science in Computer Technology August 2022 May 2024Eastern Illinois University |
