Data Analytics Real-Time

PUJITH KOTHA

+1-414-***-**** *****.**********@*****.*** https://www.linkedin.com/in/pujithkotha99/ https://github.com/kothapujith/ SUMMARY

Data analytics specialist with robust experience in real-time monitoring, visualization, and analysis for large-scale systems. Proficient in leveraging Prometheus, Grafana, and Elasticsearch to monitor system health, assess uptime and downtime, and uncover user engagement patterns. Skilled in Python, SQL, and data visualization, consistently delivering actionable insights that support key business decisions. My background in Health Big Data Analytics, combined with hands-on project work, has equipped me with practical expertise and readiness to contribute effectively in this field12. SKILLS

• Programming Languages: MySQL, Java, Python, HTML, CSS, Bootstrap

• Data Analytics and Visualization Tools: Prometheus, Grafana, Elasticsearch, Power BI, Tableau, Matplotlib, Pandas

• Frameworks: Kafka, JUnit, Spring Boot, REST, Swagger, Docker

• Data And Database Tools: MySQL, Kafka-SQL, Elastic Search, Prometheus, Redis

• Web And Cloud Technologies: Confluent Cloud, Red Hat Automation Manager, Kubernetes

• Security And Authentication: Public key/Private key, OAuth

• Software/Other Technologies: Drools, Postman, Jenkins, GitHub EXPERIENCE

Tata Consultancy Services Systems Engineer – Backend June 2023 - June 2025 Real-Time Event Processing Application

• Developed producer, processor, and consumer applications using Java, Spring Boot, and Confluent Kafka to enable scalable, real-t ime data streaming and processing.

• Built and deployed KSQL applications in Confluent Cloud for stream processing and analytics.

• Integrated Grafana and Prometheus to monitor system uptime, downtime, and traffic surges, providing actionable operational insi ghts.

• Implemented Drools Rule Engine in the processor application to enable dynamic business rule changes, ensuring flexible event re sponse logic.

• Delivered real-time event streaming to both Java and React-based consumer applications using WebSockets for enhanced user int eractivity and system responsiveness.

Customer Recommendation Engine

• Designed and developed multiple microservices with varying uptimes (daily, weekly) to dynamically process and score customer data.

• Ingested and analyzed data from multiple sources (ASR, EXL, BDP, Paxcomm) using Kafka, aggregating customer touchpoints f or comprehensive scoring and personalized recommendations.

• Implemented primary key-based (member ID) data retrieval and scoring logic to generate top 5 product and destination recommen dations per customer.

• Built Elasticsearch dashboards to visualize customer engagement, system health, and key analytics, supporting data-driven decisio n-making and marketing strategies.

• Enhanced customer engagement analysis by integrating additional touchpoints and leveraging real-time metrics, contributing to a 15% increase in user interaction.

EDUCATION

University of Wisconsin Sep 2023 - May 2025

Master of Science, Computer Science Milwaukee, US

• Coursework: Data Structures and Algorithms, Machine Learning, Scientific Data Visualization, Health Big data Analytics SRM Institute of Science and Technology, India Aug 2017 - May 2021 Bachelor of Science, Electronics and Communications Chennai, India PROJECT EXPERIENCE

Cancer Demographic Analysis (Python Pandas Matplotlib)

• Investigated demographic correlations with cancer incidence using 22 years of CDC data (1999-2021)

• Engineered data preprocessing pipeline reducing dataset volume by 15% while preserving critical demographic variables (age

, region, socioeconomic status)

• Applied statistical analysis to identify significant correlations between geographic regions and cancer prevalence rates

• Developed temporal heatmaps visualizing cancer mortality trends across demographic segments

• Key Insight: Identified 23% higher incidence in Midwestern states among 55-70 age cohort EEG-Based Biometric Classification (Python Scikit-learn)

• Implemented feature engineering pipeline extracting alpha-band power and cross-channel coherence metrics

• Trained Random Forest classifier achieving 92.3% eye-state prediction accuracy

• Conducted SHAP value analysis to identify parietal electrodes as most significant predictors

• Created topographic brain maps correlating electrode positions with classification confidence

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