| | | 20,000+ Fresh Resumes Monthly | |
|
|
| |
Click here or scroll down to respond to this candidateCandidate's Name
Data AnalystPHONE NUMBER AVAILABLE EMAIL AVAILABLE Charlotte, NC LinkedInProfessional SummaryAround 3 years of hands-on experience as a Data Analyst, leveraging Python, SQL, Tableau, Power BI, cloud platforms (AWS, Azure), and ETL tools to extract, transform, and load data for insightful analysis and reporting.Practical understanding of Data modeling (Dimensional and relational) concepts like Star-Schema Modeling, Snowflake Schema Modeling, and Fact and Dimension tables.Experience in data integration, data wrangling, ETL/ ELT processes, Data Interpretation, and Data Integration by using SQL.Expertise in Python and R libraries (ggplot2, Pandas, NumPy, Seaborn, SciPy, Matplotlib, TensorFlow) for efficient model development and insightful data analysis.Proficient in designing and implementing various DBT models to define the data transformation logic, ensuring data accuracy and consistency across the data warehouse.SkillsLanguage: Python, R, SQLIDEs: PyCharm, Jupyter NotebookVisualizations: Tableau, Power BI, Looker, ExcelMachine Learning: Statistical Modeling, Time-series Regression, Predictive analysis, Clustering (K-Means), SVMETL and Cloud: SSIS, SSAS, Informatica Power Center, AWS (DynamoDB, S3, EC2, Redshift), Azure (Azure Data Factory, Azure DevOps, Azure Synapse)Packages: NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, Keras, TensorFlow, Ggplot2Database: SQL Server, MySQL, PostgreSQL, MongoDB, Snowflake, Teradata, BigQueryTools: SQL Server Management Studio (SSMS), Data Build Tool (DBT)EducationMasters in Computer ScienceUniversity of North Texas, Denton, TXExperienceHumana, TX Jan 2024 PresentData AnalystEnhanced data quality by 20% by developing and implementing data cleansing and validation routines in the Data Build tool (DBT).Executed SQL queries for extracting healthcare data, achieving a 20% improvement in extraction speed compared to previous methods.Built and optimized AWS Redshift Queries to provide real-time insights to stakeholders, leading to a 15% increase in data-driven decision-making.Established and maintained SSIS packages to extract, transform, and load (ETL) data from various sources (databases, flat files, APIs) into data warehouses, improving data processing efficiency by 20%.Streamlined data workflows by 30% through the development of automated processes in Alteryx, freeing up valuable time for more strategic data analysis.Utilized machine learning algorithms and advanced statistical analysis (decision trees, regression models, SVM, clustering) with the scikit-learn package in Python.Increased data-driven decision-making by 25% by creating visually appealing Tableau dashboards that effectively communicated key performance indicators (KPIs).Cognitive Healthcare Solutions, India Jul 2020 Jun 2022Data AnalystLeveraged Python libraries such as Pandas, NumPy, Scikit-learn (Random Forests), and Matplotlib to perform comprehensive data analysis, including data cleaning, exploration, visualization, feature selection, and engineering.Wrote and optimized complex SQL queries to extract and transform large datasets from relational databases (MySQL, PostgreSQL), retrieving 1 million records for analysis.Built ETL pipelines using Azure Data Factory to extract, transform, and load data from various sources into Azure data stores, automating data integration processes and reducing manual effort by 50%.Developed and implemented predictive models using machine learning algorithms such as linear regression, classification, K-means clustering, and regularization for data analysis.Improved patient outcomes by 15% by identifying trends and patterns in healthcare data using Power BI, enabling targeted interventions and preventative measures.Achieved a 30% improvement in data management outcomes by optimizing data storage, retrieval, and processing within the Azure Data Lake.Exploited Snowflake to automate ETL workflows, and streamline the ingestion and transformation of billions of rows of data, significantly minimizing manual effort and improving operational efficiency by 70%. |
