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Click here or scroll down to respond to this candidateCandidate's Name
Phone: PHONE NUMBER AVAILABLEEmail: EMAIL AVAILABLELinkedIn: LINKEDIN LINK AVAILABLESUMMARYDetail-oriented Data Analyst with 6 years of experience in ensuring data quality, accuracy, and consistency. Proven expertise in developing and implementing data quality standards, profiling, cleansing, validation, and enrichment techniques. Strong analytical skills with proficiency in data analysis, visualization, and Python scripting. EDUCATIONUniversity of North Carolina at CharlotteMaster of Science in Computer Science, GPA: 4.0/4.0 Jan 2019 - May 2020Jawaharlal Nehru Technological University, Kakinada Bachelor of Engineering in Information Technology, GPA: 3.5/4.0 Sep 2012 - May 2016TECHNICAL SKILLS Data Quality Tools & Techniques: Data Profiling, Data Cleansing, Data Validation, Data Enrichment Data Analysis & Visualization: Python, Pandas, NumPy, SciPy, Scikit-learn, Matplotlib, Jupyter Notebook, Google Data Studio, Power BI, Tableau Databases & ETL: SQL, MySQL, ETL Processes Software & Tools: Robot Framework, Postman, Jira, TestRail, Confluence, Crucible, Microsoft Office Methodologies: Agile, QA Standards, Test Plans & Strategies EXPERIENCEData Operations ManagerNavvis HealthCare, St. Louis, MODec 2020 - Aug 2024 File Importation and Data Integration: Designed and implemented SSIS packages for importing various healthcare-related file types (e.g., CSV, Excel, XML) into SQL Server databases, ensuring the integrity and accuracy of patient, clinical and provider data throughout the ETL process. ETL Process Design: Created and maintained complex SSIS packages to automate the import and transformation of large healthcare datasets from multiple sources, optimizing performance and reducing processing times Data Transformation and Cleansing: Applied advanced data transformation logic using SSIS to cleanse, format, and standardize incoming healthcare data, ensuring compliance with industry standards and alignment with clinical and operational business requirements before loading into destination databases. Scheduled Data Imports: Automated recurring data import tasks using SQL Server Agent to schedule SSIS package executions, ensuring timely availability of critical healthcare data for reporting, analytics, and regulatory compliance. Data Quality Management: Ensured the quality, accuracy, and consistency of healthcare data by developing and implementing data quality standards, policies, and procedures. Designed, developed, and maintained data quality control processes and workflows to identify and rectify data discrepancies, ultimately enhancing patient care and operational efficiency. Data Profiling and Analysis: Conducted thorough data profiling and analysis to identify quality issues within healthcare datasets. Initiated corrective actions and led efforts to cleanse and enrich data, including deduplication, standardization, and augmentation, thereby improving the reliability of clinical and financial reporting. Data Validation: Developed and executed rigorous data validation tests using SQL to verify the accuracy and completeness of patient, claims and provider data. Maintained comprehensive documentation of data quality processes, procedures, and issue resolutions to ensure compliance with healthcare regulations and industry best practices. Stakeholder Collaboration: Collaborated closely with cross-functional healthcare teams, including IT, clinical staff, and administrators, to define data quality requirements and ensure alignment with organizational objectives. Provided training and awareness programs to educate stakeholders on the importance of data quality and their role in maintaining it. Reporting and Visualization: Implemented monitoring and reporting systems to track healthcare data quality metrics, identify issues, and drive improvements. Presented findings to senior management to inform data- driven decisions. Utilized Jupyter Notebook for advanced data analysis and visualization, and Python scripting for data calculations relevant to healthcare analytics. Financial Data Analysis: Leveraged SQL to perform financial data aggregation from various healthcare data sources, ensuring accurate data flow for financial reporting and compliance. Utilized Excel functions, including pivot tables, MATCH, LOOKUP, and aggregate formulas for data comparison. Project Management: Managed and prioritized multiple projects and support tickets simultaneously. Used SharePoint and Smartsheets to collaborate with team members and consolidate results. Prepared data for root- cause analysis meetings.Test AnalystIBM India, Bangalore, IndiaJun 2016 - Sep 2018 Manual and Automated Testing: Led manual and automated testing of various software components. Developed test strategies, plans, conditions, and executed manual test cases and automated test scripts. Testing and Quality Assurance: Conducted smoke, functional, UI, regression, system, and ad-hoc testing. Automated repetitive testing tasks using Robot Framework with Python scripting. Developed functional and regression automated scripts using RIDE. Defect Tracking and Resolution: Performed defect tracking, problem analysis, and provided status reports during daily meetings. Collaborated closely with project team members on issue resolution and process optimization.ACADEMIC PROJECTSFake Job Posting PredictionDescription: Predict which job descriptions are fraudulent or real using text data and metadata features. Roles and Responsibilities: Analyzed the Kaggle Data set using Jupyter Notebook. Leveraged Python to perform data preprocessing, feature engineering, and model prediction. Analyzed data using exploratory data analysis and used Google Data Studio for data visualization. Used H2O AutoML models (Deep Learning, Random Forest estimator, GBM) for modeling. Used RMSE, MSE, AUC, AUCPR, and Mean_per_Class_Error to validate the score. Analyzing the Impact of Skyscrapers on Climate Change Description: Analyze the effect of infrastructure modifications from tall building units/skyscrapers on climate change and develop a predictive model based on energy consumption from skyscrapers. Roles and Responsibilities: Handled Kaggles large-scale dataset with over 20 million entries using Google Colab. Leveraged Python to perform data preprocessing, feature engineering, and model prediction. Analyzed data using exploratory data analysis and used Jupyter Notebook for data visualization. Used LightGBM algorithm for modeling and Root mean square error for evaluation of the model. |
