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Data Engineer with 2+ years in Python, SQL, PySpark, ETL & Data Analytics
Data Engineer with 2 years of experience in Python, SQL, Power BI, ETL development, and data analytics. Proven ability to convert business requirements into scalable ETL workflows, interactive dashboards, and analytics solutions that drive business value. Experienced in building data pipelines, processing large datasets, and delivering data-driven insights through business intelligence and machine learning solutions. Skilled in PySpark, AWS, data modeling, and cloud-based data engineering practices to enhance operational efficiency and decision-making
Rajiv Gandhi College of Engineering and Research
B.E. · Computer Science
August 1, 2019 – June 30, 2023
Nandanwan Arts, Commerce and Science College
HSC
N/A – May 31, 2019
Shahu's Garden Convent and High School
SSC
N/A – May 31, 2017
Edverciity by Livetechskills
Data Engineer
September 1, 2024 – Present
Nagpur, Maharashtra, India
Rubixe AI Solutions Company
Data Science Intern
October 1, 2023 – August 1, 2024
Bengaluru, Karnataka, India
Smart Lead Scoring System
June 20, 2026 – Present
Designed and deployed an end-to-end ML system for lead conversion prediction, achieving 85%+ accuracy and improving sales prioritization by 40%. Developed an interactive Streamlit dashboard supporting single and bulk CSV-based predictions with real-time Plotly visualizations. Implemented secure authentication using streamlit-authenticator with bcrypt hashing, eliminating unauthorized access risks. Engineered complete data pipelines including data cleaning, EDA, and feature engineering on 30K+ synthetic leads, improving model performance by 25%. Trained and evaluated Logistic Regression, Random Forest, and XGBoost models using ROC-AUC and precision-recall metrics for optimal model selection. Integrated SQL-based logging to store prediction results, confidence scores, and metadata ensuring 100% traceability. Automated PDF and CSV report generation, reducing manual reporting effort by 70%. Utilized Python, Pandas, NumPy, Scikit-learn, PySpark, Streamlit, Plotly, SQL, and Joblib for scalable ML deployment.
View ProjectAI Job Market Insights
June 20, 2026 – Present
Developed an end-to-end machine learning system to predict AI/ML job growth trends using multiclass classification for strategic workforce planning. Performed data preprocessing on mixed-type datasets using OneHotEncoder, feature scaling, and train-test splitting, improving model stability by 20%. Conducted exploratory data analysis to evaluate salary distributions, role demand, and class imbalance, generating actionable market insights. Implemented ML pipelines with Logistic Regression, Random Forest, and XGBoost using Pipeline() and GridSearchCV() for hyperparameter optimization. Applied StratifiedKFold cross-validation to maintain balanced class distributions and reduce model bias by 15%. Achieved highest performance with XGBoost (41% accuracy), outperforming baseline models by 5%. Evaluated models using accuracy, precision, recall, F1-score, and confusion matrix to ensure robust performance validation. Extracted and visualized XGBoost feature importance to identify key predictors driving AI job market growth.
View ProjectDigital Wellbeing Pattern Analyzer
June 20, 2026 – Present
Developed a machine learning-based Digital Wellbeing Pattern Analyzer to predict digital burnout risk using behavioral and smartphone usage data. Preprocessed and engineered features from multi-dimensional datasets including screen time, sleep duration, mood, and anxiety metrics, improving model performance by 22%. Created derived behavioral features such as social_media_ratio to enhance burnout risk classification accuracy. Trained and evaluated classification models using Scikit-learn with hyperparameter tuning via GridSearchCV for optimal performance. Selected and serialized the best-performing model as burnout_model.pkl using joblib for production deployment. Built and deployed an interactive Streamlit web application enabling real-time burnout prediction and confidence scoring. Implemented a rule-based recommendation engine to generate personalized digital wellbeing suggestions based on user behavior. Utilized Python, Pandas, NumPy, Scikit-learn, Streamlit, Joblib, Git, and GitHub for end-to-end ML deployment.
View ProjectNASSCOM Certified Data Scientist
NASSCOM
June 1, 2026 – Present
IABAC Data Science Foundation Certification
IABAC
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's project diversity, ranging from lead scoring to job market insights and digital wellbeing, demonstrates a broad interest in applying data engineering and machine learning across different domains. The current role as a Data Engineer at Edverciity by Livetechskills, which involves mentoring, suggests a collaborative and knowledge-sharing mindset. The skills listed align well with the target role, indicating a focused career path.
Soft Skills & Operational Fit
The candidate's experience in collaborating with stakeholders and mentoring students suggests good communication and teamwork skills. The project descriptions indicate an ability to work on end-to-end solutions, which aligns with operational fit for a Data Engineer role. The mention of Agile methodology in technical skills also points to an understanding of modern development practices.