Jayanth Tirumala

Entry-level Data Analyst with a strong foundation in Python, SQL, and Excel. Skilled in data cleaning, exploratory data analysis, visualization, and reporting. Able to transform raw data into actionable insights and eager to support data-driven decision-making in a professional environment.

• Developed a data-driven Windows activity monitoring system to analyze application usage, idle time, and productivity metrics in hybrid work environments.
• Built data ingestion, cleaning, validation, and aggregation pipelines using Python (FastAPI) and SQL (PostgreSQL) to manage time-series activity data.
• Performed exploratory analysis and reporting, generating analytics-ready datasets for dashboards and usage reports.
• Designed database structures to support longitudinal analysis and trend identification.
• Collaborated on secure data access implementation using Azure AD (OAuth 2.0, SSO, RBAC) and monitored services via Azure Monitor.

Role: Team Lead | System Designer

• Designed an end-to-end data pipeline capturing voltage, current, and load pattern data from IoT (Arduino) sensors.
• Implemented real-time data acquisition and preprocessing to support fault detection and anomaly analysis.
• Analyzed sensor datasets using rule-based logic to identify power theft patterns and abnormal consumption behavior.
• Structured data storage and logging formats to enable historical analysis, trend comparison, and reporting.
• Generated alert-ready datasets to support monitoring dashboards and usage reports.

Role: Project Lead | Focus: Data Monitoring & Predictive Alerting

• Developed a real-time monitoring system to detect unsafe voltage and current fluctuations and trigger automated load protection.
• Designed data collection and event-logging architecture for tracking electrical anomalies over time.
• Prepared the system for future IoT and cloud integration, enabling remote data logging, alerts, and centralized monitoring.
• Defined event classification and pattern recognition logic to support predictive maintenance and anomaly detection.
• Proposed dashboard visualizations for real-time and historical analysis aligned with smart grid monitoring models.