Aspiring Data Analyst & Data Engineer
B.Tech in Materials Science & Engineering | Python • SQL • ETL Pipelines • Data Visualization • Cloud
I’m passionate about transforming raw data into actionable insights and building scalable data systems.
With a strong analytical foundation and a growing interest in data engineering, I aim to bridge the gap between data analysis, automation, and infrastructure.
Currently, I’m exploring data pipeline automation, dashboarding, and recruitment analytics projects — combining my coding and analytical skills to solve real-world problems.
Languages & Libraries: Python (Pandas, NumPy, Matplotlib, Seaborn), SQL, OpenCL, MATLAB
Data Engineering: ETL/ELT Pipelines, Data Cleaning, Workflow Automation, Scheduling
Visualization: Power BI, Streamlit, Matplotlib, Seaborn
Databases: MySQL, PostgreSQL
Cloud & Tools: AWS (S3, Lambda), Git, GitHub, CI/CD basics
Other: Jupyter Notebook, Excel Analytics, REST API Integration
Goal: Analyze recruitment datasets to uncover insights about hiring patterns, skill demands, and candidate success rates.
Highlights:
- Conducted in-depth exploratory data analysis (EDA) using Python to identify key recruitment trends.
- Developed dashboards showing application success rates, top hiring industries, and candidate experience patterns.
- Demonstrated ability to convert raw HR data into meaningful, insight-driven visuals.
Tech: Python, Pandas, Matplotlib, Seaborn, Power BI
Goal: Design an end-to-end automated data pipeline for processing and visualizing financial data in real time.
Highlights:
- Built a modular ETL pipeline automating data extraction, cleaning, and transformation using Python and SQL.
- Created an interactive financial performance dashboard with live updates and trend analysis.
- Integrated scheduled automation to mimic real-world pipeline orchestration (Airflow/Cron).
- Showcases strong data engineering principles and pipeline optimization skills.
Tech: Python, Pandas, SQL, Streamlit, AWS S3, Airflow (or Cron)
Goal: Perform healthcare data analysis to reveal insights into patient outcomes and treatment patterns.
Highlights:
- Processed large-scale patient datasets to identify correlations between treatment plans and outcomes.
- Built visual analytics dashboards showing disease trends and health performance indicators.
- Focused on data preprocessing, feature extraction, and visualization accuracy.
Tech: Python, Pandas, NumPy, Matplotlib, Seaborn, Jupyter Notebook
I love building systems that not only analyze data but also automate insights — reducing human effort while boosting decision-making power.
Thanks for visiting! I’m always open to collaborations, internships, and opportunities in data analytics or data engineering.