Data Analyst / September, 2024 — Present

Designed and executed ETL workflows to query and transform 5M+ daily sales inventory records using SQL on Amazon Redshift ensuring 99% data accuracy and delivering timely insights for store managers and Finance teams. Collected and integrated sales data from multiple sources including Point of Sale (POS) systems e-commerce platforms and customer relationship management (CRM) systems using custom SQL queries for extraction and API integration where needed. Designed reporting datasets and KPIs in Power BI with DAX which streamlined analytics and cut dashboard refresh times by 35% accelerating decision-making speed. Built and maintained interactive Power BI dashboards to monitor regional sales trends seasonal performance and inventory turnover helping reduce stock-outs by 15%. Performed root-cause analyses on data discrepancies between POS CRM and e-commerce systems enhancing data alignment across omnichannel reporting. Performed Exploratory Data Analysis (EDA) in Python (Pandas NumPy Matplotlib) to identify hidden trends and anomalies supporting seasonal promotions that increased revenue by 10%. Guided business stakeholders through dashboard insights providing data-driven recommendations that influenced pricing adjustments markdown strategy and product promotion timing. Conducted AB testing on pricing and promotional campaigns using Python and statistical methods to generate insights that boosted marketing ROI by 12%. Developed demand-forecasting models using Python and historical sales data to support inventory planning for seasonal and high-velocity products. Automated recurring weekly and monthly reports using Python scripts with S3-based storage and Alteryx workflows saving 15+ hours per week and reducing manual reporting errors by 25%. Performed cost-benefit analysis to prioritize automation areas reducing manual errors by 25% and improving analyst productivity. Managed data warehouses on Google Cloud Platform (GCP) using BigQuery for scalable storage efficient data retrieval and improved reporting performance. Implemented data-quality validation rules across ETL workflows identifying anomalies early and improving trust in reporting outputs across finance and operations teams. Collaborated in Agile sprints using Jira for tracking requests and Confluence for documenting dashboard logic and reporting workflows improving team collaboration and audit readiness.

Data Analyst / January, 2021 — August, 2023

Collaborated with clinical compliance and IT teams transforming analytical findings into actionable insights that supported regulatory reporting and executive dashboards. Extracted and consolidated structured and unstructured patient data from EHR systems with 97% accuracy using SQL Python and Databricks ensuring HIPAA-compliant datasets for clinical and regulatory reporting. Developed interactive Tableau dashboards to visualize patient records claim submissions and operational KPIs which improved data entry accuracy by 25% and reduced manual reporting by 30%. Built cloud-integrated dashboards in Looker to support real-time clinical data insights streamlining access to performance metrics for operations and compliance teams. Designed optimized data models and star schemas in Snowflake reducing data redundancy by 30% and accelerating reporting performance for large-scale clinical data. Maintained documentation for EHR data pipelines including schema definitions metadata tracking and data-flow diagrams to enhance future scalability and onboarding. Queried and optimized SQL scripts in Snowflake and MySQL improving report generation speed by 40% and enabling faster decision-making for healthcare operations. Conducted variance and trend analyses on multi-source EHR and claims data enabling early identification of documentation errors and process gaps. Automated Python scripts and Alteryx workflows to clean and process EHR data storing processed datasets and reports in AWS S3 for secure HIPAA-compliant historical analysis. Conducted Exploratory Data Analysis (EDA) using Python (Pandas Matplotlib) and Databricks notebooks to identify trends and insights in patient data leading to a 25% improvement in data accuracy through process optimization. Conducted data profiling validation and cleaning using Python Alteryx and Advanced Excel (Pivot Tables VLOOKUP Power Query) which enhanced data quality and reduced reporting errors by 20%.