•Implemented SQL queries to retrieve, filter, and aggregate data from relational databases, enabling comprehensive analysis and reporting of key metrics and trends.•Utilized R's ggplot2 package to create visually appealing data visualizations, resulting in a 25% improvement in stakeholders' understanding of complex data patterns and trends.•Applied Python libraries such as Pandas and NumPy to perform advanced data manipulation tasks, such as merging datasets, handling missing values, and creating derived variables, facilitating deeper insights and analysis.•Implemented Tableau's features to perform advanced data analysis and create dynamic visualizations tailored to specific business requirements, resulting in a 30% increase in data-driven decision-making.•Utilized Excel's Power Query feature to import, clean, and transform data from various sources, streamlining the data preparation process..•Designed and developed complex Tableau dashboards incorporating multiple data sources, leading to a 25% improvement in data visualization effectiveness.•Managed MySQL databases, implementing data modeling techniques and performance optimizations, resulting in a 15% increase in database efficiency.•Implemented AWS EC2 instances to deploy and manage scalable computing resources, facilitating efficient data processing and analysis workflows.•Utilized AWS S3 and DynamoDB to establish robust data storage solutions, increasing data storage capacity by 35% and supporting real-time data processing and analytics.•Contributed to Agile teams, participating in sprint planning, stand-ups.•Conducted A/B testing and time series analysis, leading to data-driven insights that guided decision-making processes and improved marketing campaign effectiveness by 25%.•Played a key role in data warehousing initiatives, designing and optimizing data schemas, ETL workflows, and data integration processes to support reporting, analytics, and business intelligence needs.

Implemented advanced statistical techniques and machine learning algorithms in R and Python to develop predictive models for business forecasting and decision support. Executed complex SQL queries for data transformations in SQL Server and Oracle databases, achieving a 20% reduction in query response time and enhanced data accessibility. Streamlined analysis procedures using Python's NumPy and Pandas libraries, improving efficiency and accuracy in data cleaning, manipulationand transformation tasks. Created interactive data visualizations with ggplot2 and Plotly in R, leveraging Dplyr for data wrangling to ensure data integrity and consistency. Designed data mining and segmentation strategies, resulting in a 20% increase in the effectiveness of targeted marketing campaigns and personalized customer experiences. Implemented data pipelines on GCP using Apache Beam and Dataflow, resulting in a 30% increase in productivity through automation of data ingestion, transformation, and loading tasks. Used Power BI and Excel's advanced features for interactive data exploration and storytelling, combining data visualization techniques with narrative elements to effectively communicate insights to stakeholders. Conducted hypothesis testing and statistical analysis, leading to a 25% improvement in strategic decision-making processes by validating business assumptions and assessing the significance of observed trends. Created interactive dashboards and reports in Power BI, integrating data from multiple sources to provide a comprehensive view of key performance indicators and business metrics, resulting in a 20% increase in data-driven decision-making. Utilized Excel's advanced functions such as VLOOKUP, INDEX-MATCH, and pivot tables to analyze large datasets and generate actionable insights for business stakeholders. Conducted regular performance evaluations and optimization of data pipelines and ETL processes, resulting in a 25% improvement in efficiency and reliability