1. Designed and implemented ETL pipelines with SSIS and Python, integrating diverse data sources into a centralized AWS Redshift cloud database to enhance data accessibility and integrity for in-depth analytics.2. Analyzed marketing, customer, and market trends data using Apache Hadoop and Spark, scaling the data processing for robust analytics and insightful reporting.3. Transformed complex data into actionable insights using SQL and Python, reporting key metrics like ROI and customer… Show more 1. Designed and implemented ETL pipelines with SSIS and Python, integrating diverse data sources into a centralized AWS Redshift cloud database to enhance data accessibility and integrity for in-depth analytics.2. Analyzed marketing, customer, and market trends data using Apache Hadoop and Spark, scaling the data processing for robust analytics and insightful reporting.3. Transformed complex data into actionable insights using SQL and Python, reporting key metrics like ROI and customer engagement on Tableau and Power BI dashboards to drive strategic decisions.4. Orchestrated and implemented Machine Learning models including Decision Trees, XGBoost, Random Forest, and Gradient Boosting improving the predictive accuracy of marketing strategies by 15%. Show less

1. Developed and optimized ETL pipelines using Apache Airflow and SSIS, integrating different data sources into a Snowflake database to streamline analytics and reporting processes.2. Leveraged Apache Spark and Databricks for efficient large-scale data processing, enhancing system performance by 30% and improving data throughput and reliability.3. Automated data transformations and integrations using Python, transitioning from manual setups to robust, scalable scripts on AWS Lambda… Show more 1. Developed and optimized ETL pipelines using Apache Airflow and SSIS, integrating different data sources into a Snowflake database to streamline analytics and reporting processes.2. Leveraged Apache Spark and Databricks for efficient large-scale data processing, enhancing system performance by 30% and improving data throughput and reliability.3. Automated data transformations and integrations using Python, transitioning from manual setups to robust, scalable scripts on AWS Lambda, reducing processing times by 20% and reducing operational costs.4. Created and managed real-time BI dashboards with AWS QuickSight, enabling dynamic monitoring of KPIs and operational metrics, which improved decision-making and business reporting accuracy by 10%. Show less

1. Developed a predictive model leveraging machine learning algorithms to simulate weather parameters, such as temperature and precipitation, to aid farmers in maximizing crop yield and reducing wastage.2. Conducted regression analysis using historical yield data, soil conditions, and weather patterns to develop a model that accurately predicts the optimal timing for sowing rice crops.