Iām a detail-oriented and motivated Data Analyst with experience in building impactful data-driven solutions across healthcare and finance sectors. I specialize in dashboard development, ETL pipelines, and workflow automation, using tools like Power BI, SQL, PowerApps, and Python. My work involves translating raw data into actionable insights for resiliency planning, cyber recovery, and fraud detection, often using technologies like Streamlit, Apache Airflow, Tableau, and AWS. I enjoy blending automation with analytics to streamline processes, ensure business continuity, and support IT strategy. A curious learner by nature, I continuously explore new technologies and enjoy solving real-world problems with code, creativity, and clean design.
Developed enterprise-grade analytics and reporting solutions by integrating data from the Operational Data Warehouse, Salesforce, and application platforms. Built Power BI dashboards for operational risk, resiliency assessment, and compliance reporting, and automated data pipelines using SQL, Power Automate, and PowerApps. Performed advanced analysis on application dependencies and infrastructure health, delivering insights that strengthened operational resilience and supported executive decision-making.
Drove the Application Impact Analysis process for business-critical applications, ensuring accurate resiliency tiering and business capability alignment. Designed interactive Power BI dashboards for cyber resiliency, disaster recovery, and technical assessments, providing visibility into system performance and risks. Automated workflows using Power Automate and chatbot solutions to streamline data movement and reduce manual operational effort across teams.
Designed a regulatory reporting simulator to automate key components of the FR Y-9C process for U.S. banks. Utilized Python and SQL for data validation, reconciliation, and capital adequacy checks. Developed an interactive Power BI dashboard to visualize capital ratios, risk-weighted assets, and reporting errors. Demonstrated strong understanding of regulatory compliance, reporting controls, and data-driven automation in financial reporting.
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Built an end-to-end ETL pipeline to process and analyze bank transactions for fraud detection. Used Python and Apache Airflow to automate daily ingestion and transformation, stored data in PostgreSQL, and applied rule-based logic to flag suspicious activities. Visualized flagged transactions with a Streamlit dashboard and deployed all components on AWS for cloud simulation.
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Built an LSTM-based model in Jupyter Notebook to analyze sentiment from financial news articles and forecast stock price trends. Applied NLP for sentiment extraction and deployed an interactive visualization dashboard using Streamlit.
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Built a full-stack web application to manage online cab bookings. Enabled users to register, book rides, and view booking history. Simulated real-time data flow with CRUD operations and implemented backend logic for user and admin operations.
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Developed a machine learning model using Python and Scikit-learn to classify news as real or fake. Used TF-IDF vectorization and a Passive Aggressive Classifier to train and evaluate the model on labeled datasets.
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