Muppalla Meghana
Career Objective
Data Engineer with 0 years of experience focused on data engineering for AI and end-to-end data products. Skilled in Python-based data preprocessing, ETL pipeline design, and basic machine learning workflows, with hands-on project experience building REST APIs and full-stack applications using Node.js, React, and MongoDB. Experienced containerizing services with Docker and managing source with Git; optimized database queries and frontend rendering to improve application performance. Conducted academic research using Python, NumPy and Pandas to model and simulate energy-harvesting systems and prepared results for publication. Holds MongoDB Associate Developer and Automation Anywhere RPA certifications; currently pursuing practical roles where I can apply data engineering pipelines, scalable APIs, and ML-ready data preparation to support analytic and AI teams.
Education
Technical Skills
Projects
- Architected a component-based React frontend to enable dynamic recipe browsing, filtering and responsive UI using JavaScript and React.
- Built Node.js RESTful APIs to support recipe CRUD, search and pagination for modular backend services.
- Optimized MongoDB queries and indexes, reducing data retrieval latency by 25% and improving page load for list views.
- Containerized backend services with Docker to standardize development environments and accelerate local testing.
- Implemented server-side endpoints and API contracts to support frontend features, documented endpoints for frontend integration using Postman collections.
- Designed system-level simulation models using Python and NumPy to evaluate energy-harvesting efficiency of triboelectric nanogenerator (TENG) configurations.
- Implemented data preprocessing pipelines with Pandas to clean, normalize and aggregate multi-sensor experimental datasets for downstream analysis.
- Developed simulation modules in C to model transient electrical characteristics and validate numerical stability under varying environmental inputs.
- Built ETL pipelines to ingest experimental logs and store structured metadata in MySQL to enable relational reporting and queryable experiment tracking.
- Applied basic machine learning models in Python to predict harvesting performance from simulated and collected data, improving model reproducibility for further experiments.
Certifications
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