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Click here or scroll down to respond to this candidate Candidate's Name
PHONE NUMBER AVAILABLEEMAIL AVAILABLEData analystPROFESSIONAL SUMMARY: Over 8 years of experience in data analysis across financial, healthcare, and retail sectors, emphasizing predictive modeling and data integrity. Proficient in utilizing Microsoft SQL Server for robust database management, ensuring optimized data retrieval and storage solutions. Demonstrated expertise in leveraging SQL for complex data querying, enhancing data extraction, and manipulation processes. Skilled in the advanced use of Excel for data analysis, report generation, and visual data presentations. Expertise in creating dynamic visualizations and business intelligence reports using Tableau to drive decision-making. Utilized Python to automate data processing tasks and perform advanced data manipulations, enhancing workflow efficiencies. Employed R for comprehensive statistical analysis and predictive modeling, supporting strategic decisions. Managed version control with GIT, facilitating team collaboration and maintaining code integrity across projects. Developed and maintained databases using SSMS, focusing on performance improvement and system monitoring. Enhanced data reporting capabilities by effectively documenting processes and maintaining high data quality standards. Experienced with SSIS for executing complex ETL processes, ensuring efficient data integration and transformation. Utilized Oracle Data Integrator and Informatica for data integration across various platforms, enhancing data consistency. Implemented Apache NiFi and Kafka for real-time data processing and streaming, optimizing data flow and accessibility. Developed secure data exchange protocols with FTP/SFTP, ensuring robust data security and compliance. Leveraged RESTful API to integrate and automate data retrieval processes across disparate systems efficiently. Expertise in Azure Stream Analytics for deploying real-time analytics solutions on cloud platforms. Managed data warehousing solutions using Azure SQL and Datawarehouse, optimizing data storage and scalability. Designed and executed data-driven strategies in the financial sector using advanced analytics to enhance risk assessment. Developed machine learning models using Amazon SageMaker, predicting financial trends and customer behaviors accurately. Proficient in employing AWS cloud services, including S3 and Redshift, for scalable and secure data solutions. Utilized Alteryx for advanced data preparation and automation, significantly reducing data processing times. Employed AWS Recognition for advanced data interpretation through machine learning, enhancing data insights. Maintained comprehensive documentation to support data management practices and ensure compliance with industry standards. Conducted in-depth data studies using the R language, supporting complex data analysis and decision-making processes. Implemented AWS Recognition to enhance data interpretation capabilities, leveraging machine learning for better accuracy.
TECHNICAL SKILLS: Database Management : Microsoft SQL Server, SSMS, Azure SQL, Oracle Data Analysis and Reporting : SQL, Excel, Tableau, Power BI, Qlik Sense, Looker, Data Studio Programming : Python, R, MATLAB Data Visualization : Matplotlib, Power BI, Qlik Sense, Looker, Data Studio ETL and Data Integration : SSIS, Oracle Data Integrator, Informatica, Apache NiFi, Kafka Machine Learning and Analytics : Amazon SageMaker, AWS Recognition, ML, R, MATLAB Cloud and Data Warehousing : AWS S3, AWS Redshift, Azure Stream Analytics, Datawarehouse Documentation and Versioning : GIT, SharePoint, Confluence Operating Systems : Linux API and Data Transfer : RESTful API, FTP/SFTPPROFESSIONAL EXPERIENCE:Client: UHG, Pittsburgh, PA Jan 2023 to till dateRole: Data analystRoles & Responsibilities:
Analyzed health care data using SQL Developed and executed complex SQL queries to extract and analyze health care data from Electronic Health Records (EHR) systems, providing actionable insights into patient outcomes and operational efficiency. Developed extensive financial reports and dashboards using Tableau, facilitating better visualization and understanding of data trends. Managed project documentation efficiently using SharePoint, ensuring easy access and collaboration across financial teams. Integrated health care data with Power BI Developed and maintained Power BI reports and dashboards to monitor key health care performance indicators, including patient readmission rates and compliance with treatment protocols. Constructed predictive models using Amazon SageMaker, effectively forecasting financial outcomes and customer behaviors. Administered AWS S3 for secure and scalable data storage, optimizing data management practices for financial datasets. Implemented AWS Redshift for data warehousing, significantly improving data retrieval and storage efficiency. Utilized Apache Spark ALS for developing recommendation systems, enhancing personalized financial services and product offerings. Developed automated business reporting experience with Tableau Created and automated comprehensive reports and visualizations in Tableau, reducing manual reporting efforts by 40% and enhancing the accessibility of critical business metrics. Conducted advanced data analysis using R language, providing deep insights into financial patterns and risk factors. Employed AWS Recognition to perform detailed image and text analysis, enhancing data interpretation capabilities. Developed advanced Crystal Reports Designed and implemented complex Crystal Reports to provide detailed business insights and analytics, enhancing data visibility and decision-making processes.Ensured secure and efficient data exchange using FTP/SFTP protocols, maintaining high standards of data integrity and security. Created health care performance metrics with Excel Designed complex Excel models and dashboards to track and analyze health care performance metrics, such as patient wait times and staff efficiency. Orchestrated real-time data streaming and processing using Apache Kafka, optimizing financial transaction monitoring. Employed Excel to perform advanced financial calculations and data manipulations, supporting detailed financial analysis and reporting. Expert in SQL Server database software Designed and optimized complex queries, stored procedures, and views in SQL Server, enhancing data retrieval efficiency and supporting advanced data analytics. Utilized AWS CloudFormation for deploying and managing cloud resources, ensuring robust infrastructure for financial applications. Integrated AWS Lambda for automating data processing tasks, increasing efficiency and reducing manual intervention. Developed user-friendly data entry forms with Visual Basic Created intuitive data entry forms and interfaces using Visual Basic for Applications, improving data collection efficiency and accuracy for internal reporting systems. Leveraged Python scripts for automating data extraction and processing tasks, enhancing workflow efficiency and accuracy. Configured and maintained AWS Redshift clusters, optimizing performance for large-scale data analytics and reporting. Utilized SharePoint to facilitate document management and collaboration, ensuring effective communication and project tracking. Developed interactive financial models and simulations using MATLAB, providing insights into financial risks and opportunities. Employed advanced analytics techniques using Tableau and Power BI, creating dynamic visualizations to depict financial trends and forecasts.Environment: SQL, Tableau, SharePoint, Confluence, Amazon SageMaker, AWS S3, AWS Redshift, Apache Spark ALS, Alteryx, R Language, AWS Recognition, FTP/SFTP, RESTful APIs, Apache Kafka, Excel, AWS CloudFormation, AWS Lambda, Microsoft Azure, Python, MATLAB, Power BI.Client: CVS Health, Miami, FL Jan 2020 to Jul 2022Role: Data Visualization AnalystRoles & Responsibilities: Developed data-driven insights for CVS Health operations Utilized SQL and Python to analyze patient data and operational metrics, providing actionable insights that improved patient care and optimized operational efficiency at CVS Health.
Created interactive dashboards using Power BI, providing actionable insights through visual analysis of sales data and customer trends. Leveraged Qlik Sense for dynamic data visualization, aiding retail managers in understanding product performance and inventory levels. Optimized report performance with Crystal Reports Improved the performance of Crystal Reports by optimizing queries and report designs, resulting in a 40% reduction in report generation time. Employed Google Data Studio to consolidate data from multiple sources, facilitating a unified view of retail operations and customer interactions. Enhanced CVS Health reporting with Power BI: Designed and implemented interactive dashboards and reports in Power BI, enabling CVS Health stakeholders to track key performance indicators and make informed business decisions.
Applied Matplotlib in conjunction with Python to produce detailed graphical representations of data, enhancing the presentation of retail analytics. Leveraged Tableau for CVS Health data visualization Created detailed visualizations and interactive dashboards in Tableau to represent CVS Health's patient engagement metrics, medication adherence rates, and service utilization trends.
Implemented data warehousing solutions with Azure, optimizing data aggregation and retrieval processes to support large-scale analytics. Utilized SQL for business reporting experience Crafted complex SQL queries and stored procedures to extract, transform, and load (ETL) data from multiple sources, supporting detailed and accurate business reports.Conducted extensive data analysis using R for statistical testing and validation, supporting marketing strategies and customer segmentation. Applied machine learning models to CVS Health data Implemented machine learning algorithms in Python to predict patient outcomes and identify trends, supporting CVS Health's initiatives in preventive care and personalized medicine. Automated data processing and reporting tasks using Python, reducing manual effort and enhancing accuracy in data handling. Enhanced data-driven decision-making by developing and maintaining complex data models and algorithms in MATLAB. Enhanced data accuracy with Crystal Reports formulas Leveraged advanced Crystal Reports formulas and functions to ensure precise data calculations and accurate reporting outcomes. Developed and maintained retail performance dashboards using Qlik Sense, providing real-time insights into sales, stock levels, and customer preferences. Developed and maintained CVS Health dashboards using Qlik Sense Created and updated dashboards in Qlik Sense to monitor CVS Health's operational performance, including pharmacy performance metrics and patient satisfaction scores. Optimized data storage and processing capabilities using Azure Datawarehouse, ensuring scalable solutions for growing retail data demands. Ensured data integrity and consistency across platforms using robust data validation techniques in SQL and Azure SQL. Utilized R to conduct hypothesis tests on retail operations data, aiding in the formulation of new sales strategies and initiatives. Managed data quality and integrity for CVS Health Employed data validation and cleaning techniques to ensure the accuracy and reliability of CVS Health's data, utilizing tools such as Excel and SQL for data management.
Employed advanced visualization techniques with Data Studio and Qlik Sense to represent complex retail analytics in an understandable format. Environment: SQL, Power BI, Qlik Sense, Looker, Google Data Studio, MATLAB, Matplotlib, Azure SQL, Azure Data Warehouse, Azure Stream Analytics, R, Python.Client: RxSense, Boston, MA. Nov 2017 to Dec 2019Role: Database AnalystRoles & Responsibilities: Integrated SQL and Oracle Data Integrator for seamless data flow between different healthcare systems, enhancing data consistency. Managed healthcare data integrations using Apache NiFi, ensuring real-time data availability and system interoperability. Utilized Informatica for complex ETL processes, streamlining data transformation and loading to improve healthcare data analytics. Employed Kafka for efficient data streaming, facilitating timely data processing and analysis in healthcare operations. Conducted data cleansing and transformations using SSIS, ensuring high data quality and reliability for healthcare reporting. Built interactive dashboards with Visual Basic in Excel: Created dynamic and interactive dashboards using VBA in Excel, enabling users to filter and analyze data efficiently, leading to more informed business decisions. Developed data documentation and governance protocols using GIT, maintaining data integrity and compliance with healthcare regulations. Managed data interactions and integrations using FTP/SFTP, securing data transfers across healthcare systems. Leveraged Linux-based systems for hosting and executing healthcare data operations, ensuring system stability and security. Utilized XML and JSON for data interchange among healthcare systems, enhancing data accessibility and interoperability. Implemented data extraction and integration processes using RESTful APIs, automating data flows and reducing manual efforts. Optimized healthcare databases using SQL tuning techniques, improving performance and data retrieval times. Streamlined healthcare data analysis and reporting processes using Excel pivot tables and advanced functions. Configured and maintained Oracle databases, ensuring robust data management and security in healthcare environments. Developed and maintained healthcare data pipelines using Apache NiFi, enhancing data accuracy and timeliness. Automated repetitive data tasks in healthcare operations using Python scripting, increasing efficiency and reducing errors. Implemented robust data security measures using GIT version control, safeguarding sensitive healthcare information. Facilitated data-driven decision-making by creating and maintaining dynamic dashboards and reports using Microsoft Excel.
Environment: SQL, Oracle Data Integrator, Apache NiFi, Informatica, Kafka, SSIS, Microsoft Excel, GIT, FTP/SFTP, Linux, XML, JSON, RESTful APIs, Python, Oracle Databases.Client: Inautix Technologies India Pvt Ltd, Chennai, India Mar 2016 to Oct 2017Role: Data analystRoles& Responsibilities:
Managed and optimized Microsoft SQL Server databases, enhancing performance and security for financial transaction processing. Developed complex T-SQL scripts for data manipulation, improving data processing efficiency and reporting capabilities. Utilized SSMS for database administration, optimizing queries and maintenance tasks to enhance system performance. Created detailed financial reports using Excel, providing insights into transaction trends and operational efficiencies. Maintained comprehensive system documentation, ensuring best practices and compliance with data management standards. Developed and maintained GIT repositories for code versioning, enhancing collaboration and code quality in project development. Monitored database performance using SQL Server tools, identifying and resolving issues to maintain system reliability. Utilized Excel for advanced data analysis tasks, including pivot tables and data visualization for stakeholder reporting. Implemented data backup and recovery procedures using SQL Server, ensuring data integrity and availability. Conducted regular data audits using T-SQL, ensuring compliance with regulatory standards and data accuracy. Automated data workflows using Python scripting, improving operational efficiency and reducing manual data handling. Optimized data queries and indexes using SSMS, enhancing database performance and user query response times. Facilitated team collaboration and project tracking using GIT, improving development processes and outcomes. Developed SQL-based solutions to handle large datasets, enhancing data analysis and business intelligence capabilities. Enhanced data security measures through rigorous SQL Server configurations and regular system audits.Environment: Microsoft SQL Server, SSMS, Excel, GIT, Python, SQL Server tools. |
