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Senior Data Analysis with 5+ years in data analysis, business intelligence & reporting.
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Data Analyst with 5 years of experience in data analysis, business intelligence, and reporting. Proficient in SQL, Python, Power BI, and Advanced Excel for data extraction, transformation, analysis, and visualization. Experienced in Exploratory Data Analysis (EDA), data modeling, ETL processes, and dashboard development. Skilled at transforming complex datasets into actionable insights that support business growth and strategic decision-making. Strong expertise in data quality, query optimization, performance improvement, and stakeholder collaboration.
PAC Ramasamyraja Polytechnic College
Diploma Mechanical Engineering · Mechanical Engineering
January 1, 2015 – January 1, 2018
RAMCO INDUSTRIES LIMITED
Data Analyst
February 1, 2021 – Present
India
Cardiac Health Monitoring and ECG Analysis Using Data Visualization Dashboards
March 1, 2024 – May 1, 2026
Developed a healthcare analytics solution to monitor cardiac health and analyze Electrocardiogram (ECG) data through interactive data visualization dashboards. The project focused on collecting, processing, and integrating patient cardiac data from multiple healthcare sources to support clinical monitoring and decision-making. Designed and implemented ETL pipelines to transform raw ECG signals and patient records into structured datasets for analysis. Leveraged SQL and Python to perform data cleansing, validation, trend analysis, and anomaly detection on heart rate and ECG measurements. Created dynamic dashboards and KPI reports to visualize patient health indicators, ECG patterns, cardiac risk metrics, and treatment outcomes. The solution enabled healthcare professionals to monitor patient conditions in real time, identify potential abnormalities, and improve overall clinical performance through data-driven insights while ensuring data quality, security, and compliance with healthcare standards. Collected and integrated ECG signals, patient health records, and cardiac monitoring data from multiple healthcare data sources. Designed and developed ETL pipelines to extract, transform, and load cardiac health data into centralized analytical repositories. Performed data cleansing, preprocessing, and validation to ensure the accuracy and reliability of ECG and patient datasets. Built scalable data models to support cardiac health analytics, patient monitoring, and clinical reporting requirements. Analyzed ECG waveforms, heart rate patterns, and cardiac health indicators to identify trends, anomalies, and risk factors. Developed interactive dashboards and visual reports to monitor patient health status, ECG metrics, and clinical performance KPIs. Automated data processing and reporting workflows to improve operational efficiency and reduce manual intervention. Utilized SQL and Python to perform trend analysis, statistical analysis, and anomaly detection on large-scale healthcare datasets. Implemented data quality monitoring frameworks to ensure consistency, completeness, and integrity of clinical data. Optimized database queries and data processing workflows to improve reporting performance and dashboard responsiveness.
End-to-End Financial Data Analytics and Visualization Framework
December 1, 2021 – February 1, 2024
Developed a comprehensive financial data analytics and visualization framework to streamline the collection, integration, processing, and analysis of enterprise financial data. The project focused on consolidating data from multiple sources, including transactions, revenue, expenses, budgets, and operational systems, into a centralized analytics platform. Designed and implemented scalable ETL pipelines to automate data ingestion, transformation, and validation processes, ensuring data accuracy and consistency across reporting systems.Built interactive dashboards and KPI scorecards to provide real-time visibility into key financial metrics such as revenue growth, profit margins, operating expenses, cash flow, budget utilization, and business performance. Leveraged SQL and Python for data extraction, cleansing, trend analysis, variance analysis, and financial forecasting. Developed data models and reporting solutions that enabled stakeholders to monitor financial health, identify business opportunities, optimize operational efficiency, and support strategic decision-making through data-driven insights. Utilized SQL and Python to analyze financial trends, identify business patterns, and generate actionable insights. Conducted variance analysis, profitability analysis, and budget performance assessments to support strategic planning. Automated recurring financial reports and dashboard refresh processes to improve reporting efficiency and reduce manual effort. Implemented data quality frameworks and validation checks to maintain reliable financial reporting standards. Optimized SQL queries, database structures, and ETL workflows to enhance data processing performance and scalability. Developed KPI monitoring solutions to track key business metrics, financial targets, and organizational performance. Collaborated with finance teams, business stakeholders, and management to gather reporting requirements and deliver analytical solutions. Generated executive-level reports and visualizations to support data-driven business decisions and performance reviews. Performed root cause analysis on revenue fluctuations, cost variances, and financial performance trends. Supported forecasting and financial planning activities through historical data analysis and predictive reporting. Maintained technical documentation for ETL processes, data models, dashboard logic, and reporting standards. Ensured compliance with organizational data governance policies, security standards, and financial reporting requirements.
Cultural Fit Analysis
The candidate's project experience spans two distinct domains: healthcare analytics (Cardiac Health Monitoring) and financial data analytics (End-to-End Financial Data Analytics). This diversity indicates adaptability and a broad interest in applying data analysis skills across different industries. The emphasis on collaboration with finance teams, business stakeholders, and management, as well as ensuring compliance with data governance policies, suggests a team-oriented and responsible approach to work. The breadth of technical skills listed (SQL, Python, Power BI, ETL tools, Spark) also points to a candidate who is open to learning and applying various technologies, which is a positive indicator for cultural fit in dynamic environments. However, the lack of community involvement or open-source contributions limits the assessment of broader cultural engagement.
Soft Skills & Operational Fit
The candidate's resume highlights problem-solving, stakeholder management, communication, and requirement gathering as soft skills. The project descriptions demonstrate practical application of these skills through collaboration with finance teams and healthcare professionals, and the ability to translate complex data into actionable insights for business decision-making. The focus on automating workflows and optimizing performance indicates an operational mindset. However, without specific assessment data on these soft skills, a deeper evaluation of their proficiency and fit within a team environment is limited.