• Analyzed large datasets (500K+ rows) using SQL and Python, achieving 30% improvement in data accuracy and consistency.• Developed and automated data pipelines on Amazon Web Services (AWS), reducing manual data processing time by 40%.• Established interactive Tableau dashboards, helping stakeholders visualize sales trends and increase revenue by 15%.• Collaborated with cross-functional teams to clean and integrate data from multiple sources (CRM, ERP) into Redshift, enabling seamless data accessibility.• Leveraged Apache Airflow to schedule and automate data workflows, reducing ETL cycle time by 50%.• Designed Power BI reports to track KPIs and business metrics, cutting reporting preparation time by 30%.• Conducted advanced statistical analysis to support business decision-making, contributing to a 10% reduction in operational costs.

• Led the development of a customer churn prediction model using Python and SQL, improving churn accuracy by 20%.• Managed large datasets in Google BigQuery and performed data transformations, increasing processing efficiency by 35%.• Migrated legacy data systems to Snowflake on the cloud, improving data retrieval times by 45%.• Integrated cloud data sources using Talend to build scalable and reliable data pipelines, enhancing data processing by 50%.• Formulated Power BI dashboards with real-time data from Google Cloud Platform (GCP) for executive reporting, reducing decision turnaround time by 25%.• Implemented machine learning algorithms to identify and mitigate customer churn, resulting in a 10% increase in retention.• Collaborated with the cloud engineering team to optimize cloud data storage costs, saving 15% on infrastructure expenses.